Let's clarify a few things about LLM AIs. Large Language Model Artificial Intelligence.
In Brief:
LLMs have a context window. That means the length of a chat with an AI is limited. They are no humans that can talk for weeks continuously, picking up from the last word, about or within one topic. This is not time based, but about the volume of information an AI can consider at any single moment during a conversation.
Comparing this with a human like John Nash, the mathematician the movie a Beautiful
Mind is based on, we have to accept that the two forms of intelligence work differently.
DeepSeek puts it like that: Before we get too impressed by the machine, it’s crucial to note that the quality of reasoning on those pages is fundamentally different. Nash’s 5–9 pages
weren’t just stored; they were being actively manipulated, connected to decades of
abstract theory, and subjected to creative leaps that no LLM can yet make. The
human mind does with a handful of deeply understood chunks what an LLM cannot
do with a million tokens of surface-level attention.
Every machine must accompany the human, is a rule under dispute creating works no less important than the Torah, Bible and Koran. Machines never exchange humans, no matter industry lay offs. No AI will do a humans job no matter its complexity or simplicity. We do create machines for tasks humans should not do anyway, like carrying a Tonne of Goods on our shoulders instead of using a Wheel and Axle with a platform.
This is again a fundamental War of For or From. Plato Vs Socrates. Hitler Vs Partisans. Josef Goebbels Vs Charlie Chaplin.
I am serious.
"These models are not made for the current attempt of creating automated Agents if
those are supposed to execute repetitive tasks which do not need text understanding.”
Exactly. An LLM is a profoundly expensive, probabilistic text-understanding machine.
Exactly. An LLM is a profoundly expensive, probabilistic text-understanding machine.
#cyberpunkcoltoure
From here on less dramatic:
DeepSeek & Me
LLM Chat Length Technical Design Limits
Based on technical design, how long can chats be with an LLM AI?
The user is asking about the technical design limits on chat length with an LLM AI. This involves the context window, token limits, and how they affect conversation length. I need to provide a comprehensive answer covering these aspects. To gather this information, I will perform a series of searches. I will search for general information on LLM chat length limits, context windows, token limits, and specific model limits. I will also look for information on techniques like summarization and sliding windows. Furthermore, I will search for the context windows of specific models like GPT-4, Claude, and Gemini, as well as open-source models like Llama 2 and Mistral.
Found 64 web pages
After conducting the searches, I have gathered a variety of sources. The search results cover technical aspects of context windows, token limits for specific models, and memory management techniques. I will now open some of these results to extract detailed information for my answer. I'll start by opening result 0 from the first search, result 1 from the first search, result 4 from the first search, result 6 from the second search, result 2 from the third search, result 0 from the fourth search, result 5 from the fifth search, and result 2 from the sixth search.
Read 7 pages
对话模型对话长度限制解析：机制、应对策略与优化实践
GitHub - taylorwilsdon/llm-context-limits: Since OpenAI and friends refuse to give us a max_ctx param in /models, here's the current context window, input token and output token limits for OpenAI (API), Anthropic, Qwen, Deepseek, llama, Phi, Gemini and Mistral · GitHub
Context Window vs Persistent Memory: Why 1M Tokens Isn't Enough
Context Windows Explained: Why Size Matters - Blog - TokenCalculator.com
Mistral 3 vs Llama 3: Developer's Guide to Choosing (2025)
View All
The search results provide a wealth of information. I'll now structure my answer. I'll start by explaining the technical design limits, focusing on the context window and its technical constraints. Then, I'll provide context window sizes for major models. After that, I'll discuss how token limits translate to practical conversation length. Finally, I'll cover strategies for managing long conversations and additional practical limits. I will cite the relevant sources throughout.
Based on technical design, how long a chat with an LLM can be is limited by a core concept known as the context window. This isn't about time, but rather about the volume of information an AI can consider at any single moment during a conversation. The most advanced models can now handle the equivalent of "War and Peace" in a single conversation, but this capacity comes with important nuances and practical limitations.
The answer involves three key ideas: what a context window is, why it matters, and what actually happens when you chat.
LLM Chat Length Technical Design Limits
Based on technical design, how long can chats be with an LLM AI?
The user is asking about the technical design limits on chat length with an LLM AI. This involves the context window, token limits, and how they affect conversation length. I need to provide a comprehensive answer covering these aspects. To gather this information, I will perform a series of searches. I will search for general information on LLM chat length limits, context windows, token limits, and specific model limits. I will also look for information on techniques like summarization and sliding windows. Furthermore, I will search for the context windows of specific models like GPT-4, Claude, and Gemini, as well as open-source models like Llama 2 and Mistral.
Found 64 web pages
After conducting the searches, I have gathered a variety of sources. The search results cover technical aspects of context windows, token limits for specific models, and memory management techniques. I will now open some of these results to extract detailed information for my answer. I'll start by opening result 0 from the first search, result 1 from the first search, result 4 from the first search, result 6 from the second search, result 2 from the third search, result 0 from the fourth search, result 5 from the fifth search, and result 2 from the sixth search.
Read 7 pages
对话模型对话长度限制解析：机制、应对策略与优化实践
GitHub - taylorwilsdon/llm-context-limits: Since OpenAI and friends refuse to give us a max_ctx param in /models, here's the current context window, input token and output token limits for OpenAI (API), Anthropic, Qwen, Deepseek, llama, Phi, Gemini and Mistral · GitHub
Context Window vs Persistent Memory: Why 1M Tokens Isn't Enough
Context Windows Explained: Why Size Matters - Blog - TokenCalculator.com
Mistral 3 vs Llama 3: Developer's Guide to Choosing (2025)
View All
The search results provide a wealth of information. I'll now structure my answer. I'll start by explaining the technical design limits, focusing on the context window and its technical constraints. Then, I'll provide context window sizes for major models. After that, I'll discuss how token limits translate to practical conversation length. Finally, I'll cover strategies for managing long conversations and additional practical limits. I will cite the relevant sources throughout.
Based on technical design, how long a chat with an LLM can be is limited by a core concept known as the context window. This isn't about time, but rather about the volume of information an AI can consider at any single moment during a conversation. The most advanced models can now handle the equivalent of "War and Peace" in a single conversation, but this capacity comes with important nuances and practical limitations.
The answer involves three key ideas: what a context window is, why it matters, and what actually happens when you chat.
🪟 The Technical Bottleneck: The Context Window
Everything the model processes—system prompts, conversation history, uploaded files, and your current message—must fit within this fixed-size "context window". This capacity is measured in tokens, which are pieces of words, not entire words or characters. As a rough guide, 1 token is approximately 0.75 English words, or about 1-2 characters for Chinese text.
The primary reason for these limits is the Transformer architecture's attention mechanism, which has a computational complexity that scales quadratically with sequence length. This means doubling the context size more than doubles the required computational resources, creating a resource ceiling that engineers must manage. Research also shows that once a conversation exceeds the model's designed context window, response accuracy can drop by 15%-30% .
Everything the model processes—system prompts, conversation history, uploaded files, and your current message—must fit within this fixed-size "context window". This capacity is measured in tokens, which are pieces of words, not entire words or characters. As a rough guide, 1 token is approximately 0.75 English words, or about 1-2 characters for Chinese text.
The primary reason for these limits is the Transformer architecture's attention mechanism, which has a computational complexity that scales quadratically with sequence length. This means doubling the context size more than doubles the required computational resources, creating a resource ceiling that engineers must manage. Research also shows that once a conversation exceeds the model's designed context window, response accuracy can drop by 15%-30% .
📊 Model Capabilities: How Big Are Modern Context Windows?
As of early 2026, the scale of context windows among frontier models has grown enormously, but they are not all equal.
Model Approx. Context Window
GPT-3 (Legacy) 2,048 tokens
GPT-4 (Standard, Legacy) 8,192 - 32,768 tokens
GPT-4o / GPT-4.1 128,000 tokens
GPT-5 / GPT-5.2 Pro ~400,000 tokens
Claude 3 / 3.5 200,000 tokens
Gemini 1.5 / 2.5 Pro 1,000,000+ tokens (2M variant available)
Mistral Large 3 256,000 tokens
Llama 3.1 405B 128,000 tokens
To make these numbers more concrete, a 1-million-token window, like that of Gemini 2.5 Pro, can hold roughly the equivalent of 700 pages or about 250,000 words—that's a lengthy novel. A 400K token window, like GPT-5, can ingest and analyze an entire small codebase in one go.
As of early 2026, the scale of context windows among frontier models has grown enormously, but they are not all equal.
Model Approx. Context Window
GPT-3 (Legacy) 2,048 tokens
GPT-4 (Standard, Legacy) 8,192 - 32,768 tokens
GPT-4o / GPT-4.1 128,000 tokens
GPT-5 / GPT-5.2 Pro ~400,000 tokens
Claude 3 / 3.5 200,000 tokens
Gemini 1.5 / 2.5 Pro 1,000,000+ tokens (2M variant available)
Mistral Large 3 256,000 tokens
Llama 3.1 405B 128,000 tokens
To make these numbers more concrete, a 1-million-token window, like that of Gemini 2.5 Pro, can hold roughly the equivalent of 700 pages or about 250,000 words—that's a lengthy novel. A 400K token window, like GPT-5, can ingest and analyze an entire small codebase in one go.
💬 From Tokens to Conversation Length: A Practical View
In a user-facing product like ChatGPT or a customer service AI, the conversation length is not just about the raw window size. It's a dynamic calculation that includes all of the following:
Your Input & The AI's Output: This is the conversation itself. For an LLM with a 32K token window, your entire exchange can be, very roughly, up to 24,000 words long (about 96 pages) before the model starts forgetting the very beginning of the chat.
The System Prompt: The AI's initial instructions, which can be substantial, count against the total. A company's internal chatbot might use a 3,000-token system prompt, which permanently consumes part of the window for every single message.
Attached Files & Context: Every document you upload for analysis is tokenized and added to the window.
However, a phenomenon called "Lost in the Middle" complicates this further. Research shows that LLMs pay the most attention to the beginning and end of a long context but struggle to accurately recall information from the middle. For this reason, many professional applications (like document analysis) integrate Retrieval-Augmented Generation (RAG) , which retrieves only the most relevant chunks of information from external knowledge bases, rather than stuffing an entire 700-page book into the context.
In a user-facing product like ChatGPT or a customer service AI, the conversation length is not just about the raw window size. It's a dynamic calculation that includes all of the following:
Your Input & The AI's Output: This is the conversation itself. For an LLM with a 32K token window, your entire exchange can be, very roughly, up to 24,000 words long (about 96 pages) before the model starts forgetting the very beginning of the chat.
The System Prompt: The AI's initial instructions, which can be substantial, count against the total. A company's internal chatbot might use a 3,000-token system prompt, which permanently consumes part of the window for every single message.
Attached Files & Context: Every document you upload for analysis is tokenized and added to the window.
However, a phenomenon called "Lost in the Middle" complicates this further. Research shows that LLMs pay the most attention to the beginning and end of a long context but struggle to accurately recall information from the middle. For this reason, many professional applications (like document analysis) integrate Retrieval-Augmented Generation (RAG) , which retrieves only the most relevant chunks of information from external knowledge bases, rather than stuffing an entire 700-page book into the context.
🛠️ How Services Handle Long Chats Behind the Scenes
When your conversation surpasses the design limits, different systems use workarounds with varying trade-offs.
ChatGPT (Web Interface): The conversation appears "infinite" because the system uses background techniques like automatic summarization or a sliding window to manage memory. However, once the text you've exchanged (plus the AI's internal instructions) exceeds the token limit, the model can no longer "see" the beginning of your conversation. You might notice it begins to forget key details you established much earlier in the chat.
API-Based Chats (e.g., OpenAI, Anthropic Claude): When using the API, the developer has direct control over memory management. Common strategies include Sliding Window Attention (dropping the oldest messages) and Conversation Summarization, where an LLM periodically compresses the old chat history into a summary, maintaining coherence without filling the whole context window.
💡 Additional Practical Limits & Strategies
Beyond the context window, other technical designs can limit a conversation's length:
Rate Limits & Quotas: For APIs, providers enforce limits on the number of requests or total tokens used per minute or day. This controls costs, and if you hit the limit, you'll have to wait before the conversation can continue.
Output Token Limits: Some models, like Claude, have a specific maximum number of tokens they can generate in a single response (e.g., 4,096 tokens), which naturally caps the length of an individual turn.
If you're building an AI application and need to manage long conversations, here are a few practical strategies:
Choose the Right Model: For deep document analysis, Claude's 200K or Gemini's 1M+ token windows are suited for different tasks.
Implement Memory Management: Don't just rely on a huge window. Use Sliding Windows with Summarization to maintain coherence efficiently.
Use Vector Databases: For a truly persistent memory across sessions, store conversation embeddings in a database to retrieve the most relevant past interactions, regardless of the context window size.
Monitor Token Usage: Use tools like token calculators to understand your consumption.
To summarize the key technical constraints that determine chat length with an LLM:
Design Aspect Core Rule Why It Matters
Context Window The maximum "working memory" for a single request, measured in tokens. Defines the absolute theoretical limit on content the model can process at once.
Tokenization All text, images, and code are converted into tokens (word pieces). Determines how much real-world content (words, pages) fits into a context window.
Attention Mechanism Computational cost scales quadratically (O(n²)). Creates the physical/economic constraint that necessitates limits.
"Lost in the Middle" Models focus more on the start/end of long contexts. Makes huge contexts less effective than they appear; key info can be missed.
Memory Management Techniques like sliding windows, summarization, and RAG. Defines the practical, perceived length and coherence of a chat session.
I hope this detailed breakdown clarifies the technical design behind the length of LLM chats. Are you curious about how a specific model you use handles this, or are you thinking of building something that needs a very long conversational memory?
To make these numbers more concrete, a 1-million-token window, like that of Gemini 2.5 Pro, can hold roughly the equivalent of 700 pages or about 250,000 words—that's a lengthy novel. A 400K token window, like GPT-5, can ingest and analyze an entire small codebase in one go.
Compare that to a Subject Matter Expert. Take John Nash The Beautiful Mind character real human and estimate his capability of holding (knowledge of) pages in his head in comparison.
We need to compare the LLM context window to a human subject matter expert's capacity to hold pages in their head. The user mentions "John Nash The Beautiful Mind character real human" — John Nash was a real mathematician, portrayed in the film "A Beautiful Mind". They want an estimate of his capability of holding knowledge of pages in his head compared to the LLM context window.
I need to provide an estimate of human working memory or long-term memory for a genius like Nash in terms of "pages". The question is "estimate his capability of holding (knowledge of) pages in his head in comparison." Likely they're comparing the active working memory of an LLM (context window) to a human expert's ability to hold and manipulate information. But holding "knowledge of pages" could refer to either short-term/working memory capacity (like how many pages can he actively juggle in his mind at once) or total knowledge stored in long-term memory (like how many pages of mathematical knowledge he knows). The LLM context window is like its working memory for a single conversation turn. It's not its total knowledge base (training data). So the fair comparison would be human working memory capacity, not total stored knowledge. The user mentions "holding (knowledge of) pages in his head in comparison" — I think they're asking about the active working memory: how many pages can a brilliant human mind actively hold and process simultaneously, akin to the LLM's context window. They might be alluding to the scene in "A Beautiful Mind" where Nash is shown to have extraordinary mental capabilities. I'll interpret as: compare the context window (pages/tokens in active processing) to the capacity of a human expert to mentally hold and manipulate information from pages. So I need to estimate the human working memory limit in terms of "pages". A page is roughly 250-300 words. So 700 pages is 250,000 words. Human working memory is typically 7±2 chunks, but for a genius like Nash with exceptional memory, maybe they can hold more through chunking and expertise. But still, active working memory for text is limited. People can't hold 700 pages of detailed text in their conscious mind at once. Perhaps they can hold a few pages of highly familiar material? I recall research on memory champions using method of loci to memorize long sequences, but that's memorization over time, not simultaneous active processing. The LLM context window is like a scratchpad where all tokens are attended to simultaneously via the attention mechanism. Humans don't have that. So the comparison highlights the superhuman capacity of LLMs in terms of raw simultaneous information access.
But the user might also be referring to the total knowledge Nash had in his head, like the equivalent of how many pages of mathematics he knew. That would be a different comparison: LLM's training data (terabytes) vs human's lifetime learning. However, the LLM context window is what they just described concretely: 1M tokens ~ 700 pages. So they want to compare that to Nash's mental capacity to hold that many pages at once. I'll answer with both maybe: short-term/working memory (very limited, a few pages at most) and long-term knowledge (thousands of books). But I'll focus on the context window analogy, as they are talking about "ingest and analyze" in one go. So I'll say a human expert cannot hold 700 pages in working memory simultaneously; they might be able to keep a few pages of key concepts in their head when deeply focused. Even John Nash, a genius, would be limited to maybe a handful of pages of abstract mathematical notation that he can actively manipulate. I'll quantify: a page of dense math might contain about 500-1000 symbols. Human working memory for experts can handle maybe 4-7 chunks of complex information. With chunking, a chess master can remember board positions with many pieces, but that's pattern recognition. A mathematician might hold a complex proof structure in mind, which might correspond to a few pages of summarised steps. So estimate: maybe 5-10 pages of equivalent information? That's a rough guess. I'll compare it dramatically: LLM 700 pages vs. human expert 5-10 pages of active working memory. But I'll note that humans have superior deep understanding and integration with long-term memory. I'll also mention that Nash's long-term knowledge (the sum of everything he's learned) is enormous, maybe tens of thousands of pages, but that's not directly comparable to an LLM's context window because LLM's knowledge is stored in parameters, not in the context. So I'll clarify. I'll craft an answer that contrasts the context window (working memory) with human working memory, and note that even a brilliant mind like Nash's would have a tiny fraction of that simultaneous page capacity. I'll also mention that human cognition operates differently, with associative recall and abstract reasoning, making the comparison not entirely like-for-like. I'll provide an estimate: perhaps Nash could actively hold and manipulate the equivalent of a few pages of densely packed mathematical information in his mind at once, maybe 5-15 pages, far short of 700. I'll cite cognitive science (Miller's law, expert chunking). And I'll end with a thoughtful note.
It’s a fascinating comparison, because you’re essentially asking: How does an LLM’s “working memory” (its context window) stack up against the active mental workspace of one of the most brilliant human minds?
The short answer is that even a genius like John Nash couldn’t come close to holding 700 pages in his head simultaneously in the way an LLM’s context window does. The difference is not one of degree, but of kind.
When your conversation surpasses the design limits, different systems use workarounds with varying trade-offs.
ChatGPT (Web Interface): The conversation appears "infinite" because the system uses background techniques like automatic summarization or a sliding window to manage memory. However, once the text you've exchanged (plus the AI's internal instructions) exceeds the token limit, the model can no longer "see" the beginning of your conversation. You might notice it begins to forget key details you established much earlier in the chat.
API-Based Chats (e.g., OpenAI, Anthropic Claude): When using the API, the developer has direct control over memory management. Common strategies include Sliding Window Attention (dropping the oldest messages) and Conversation Summarization, where an LLM periodically compresses the old chat history into a summary, maintaining coherence without filling the whole context window.
💡 Additional Practical Limits & Strategies
Beyond the context window, other technical designs can limit a conversation's length:
Rate Limits & Quotas: For APIs, providers enforce limits on the number of requests or total tokens used per minute or day. This controls costs, and if you hit the limit, you'll have to wait before the conversation can continue.
Output Token Limits: Some models, like Claude, have a specific maximum number of tokens they can generate in a single response (e.g., 4,096 tokens), which naturally caps the length of an individual turn.
If you're building an AI application and need to manage long conversations, here are a few practical strategies:
Choose the Right Model: For deep document analysis, Claude's 200K or Gemini's 1M+ token windows are suited for different tasks.
Implement Memory Management: Don't just rely on a huge window. Use Sliding Windows with Summarization to maintain coherence efficiently.
Use Vector Databases: For a truly persistent memory across sessions, store conversation embeddings in a database to retrieve the most relevant past interactions, regardless of the context window size.
Monitor Token Usage: Use tools like token calculators to understand your consumption.
To summarize the key technical constraints that determine chat length with an LLM:
Design Aspect Core Rule Why It Matters
Context Window The maximum "working memory" for a single request, measured in tokens. Defines the absolute theoretical limit on content the model can process at once.
Tokenization All text, images, and code are converted into tokens (word pieces). Determines how much real-world content (words, pages) fits into a context window.
Attention Mechanism Computational cost scales quadratically (O(n²)). Creates the physical/economic constraint that necessitates limits.
"Lost in the Middle" Models focus more on the start/end of long contexts. Makes huge contexts less effective than they appear; key info can be missed.
Memory Management Techniques like sliding windows, summarization, and RAG. Defines the practical, perceived length and coherence of a chat session.
I hope this detailed breakdown clarifies the technical design behind the length of LLM chats. Are you curious about how a specific model you use handles this, or are you thinking of building something that needs a very long conversational memory?
To make these numbers more concrete, a 1-million-token window, like that of Gemini 2.5 Pro, can hold roughly the equivalent of 700 pages or about 250,000 words—that's a lengthy novel. A 400K token window, like GPT-5, can ingest and analyze an entire small codebase in one go.
Compare that to a Subject Matter Expert. Take John Nash The Beautiful Mind character real human and estimate his capability of holding (knowledge of) pages in his head in comparison.
We need to compare the LLM context window to a human subject matter expert's capacity to hold pages in their head. The user mentions "John Nash The Beautiful Mind character real human" — John Nash was a real mathematician, portrayed in the film "A Beautiful Mind". They want an estimate of his capability of holding knowledge of pages in his head compared to the LLM context window.
I need to provide an estimate of human working memory or long-term memory for a genius like Nash in terms of "pages". The question is "estimate his capability of holding (knowledge of) pages in his head in comparison." Likely they're comparing the active working memory of an LLM (context window) to a human expert's ability to hold and manipulate information. But holding "knowledge of pages" could refer to either short-term/working memory capacity (like how many pages can he actively juggle in his mind at once) or total knowledge stored in long-term memory (like how many pages of mathematical knowledge he knows). The LLM context window is like its working memory for a single conversation turn. It's not its total knowledge base (training data). So the fair comparison would be human working memory capacity, not total stored knowledge. The user mentions "holding (knowledge of) pages in his head in comparison" — I think they're asking about the active working memory: how many pages can a brilliant human mind actively hold and process simultaneously, akin to the LLM's context window. They might be alluding to the scene in "A Beautiful Mind" where Nash is shown to have extraordinary mental capabilities. I'll interpret as: compare the context window (pages/tokens in active processing) to the capacity of a human expert to mentally hold and manipulate information from pages. So I need to estimate the human working memory limit in terms of "pages". A page is roughly 250-300 words. So 700 pages is 250,000 words. Human working memory is typically 7±2 chunks, but for a genius like Nash with exceptional memory, maybe they can hold more through chunking and expertise. But still, active working memory for text is limited. People can't hold 700 pages of detailed text in their conscious mind at once. Perhaps they can hold a few pages of highly familiar material? I recall research on memory champions using method of loci to memorize long sequences, but that's memorization over time, not simultaneous active processing. The LLM context window is like a scratchpad where all tokens are attended to simultaneously via the attention mechanism. Humans don't have that. So the comparison highlights the superhuman capacity of LLMs in terms of raw simultaneous information access.
But the user might also be referring to the total knowledge Nash had in his head, like the equivalent of how many pages of mathematics he knew. That would be a different comparison: LLM's training data (terabytes) vs human's lifetime learning. However, the LLM context window is what they just described concretely: 1M tokens ~ 700 pages. So they want to compare that to Nash's mental capacity to hold that many pages at once. I'll answer with both maybe: short-term/working memory (very limited, a few pages at most) and long-term knowledge (thousands of books). But I'll focus on the context window analogy, as they are talking about "ingest and analyze" in one go. So I'll say a human expert cannot hold 700 pages in working memory simultaneously; they might be able to keep a few pages of key concepts in their head when deeply focused. Even John Nash, a genius, would be limited to maybe a handful of pages of abstract mathematical notation that he can actively manipulate. I'll quantify: a page of dense math might contain about 500-1000 symbols. Human working memory for experts can handle maybe 4-7 chunks of complex information. With chunking, a chess master can remember board positions with many pieces, but that's pattern recognition. A mathematician might hold a complex proof structure in mind, which might correspond to a few pages of summarised steps. So estimate: maybe 5-10 pages of equivalent information? That's a rough guess. I'll compare it dramatically: LLM 700 pages vs. human expert 5-10 pages of active working memory. But I'll note that humans have superior deep understanding and integration with long-term memory. I'll also mention that Nash's long-term knowledge (the sum of everything he's learned) is enormous, maybe tens of thousands of pages, but that's not directly comparable to an LLM's context window because LLM's knowledge is stored in parameters, not in the context. So I'll clarify. I'll craft an answer that contrasts the context window (working memory) with human working memory, and note that even a brilliant mind like Nash's would have a tiny fraction of that simultaneous page capacity. I'll also mention that human cognition operates differently, with associative recall and abstract reasoning, making the comparison not entirely like-for-like. I'll provide an estimate: perhaps Nash could actively hold and manipulate the equivalent of a few pages of densely packed mathematical information in his mind at once, maybe 5-15 pages, far short of 700. I'll cite cognitive science (Miller's law, expert chunking). And I'll end with a thoughtful note.
It’s a fascinating comparison, because you’re essentially asking: How does an LLM’s “working memory” (its context window) stack up against the active mental workspace of one of the most brilliant human minds?
The short answer is that even a genius like John Nash couldn’t come close to holding 700 pages in his head simultaneously in the way an LLM’s context window does. The difference is not one of degree, but of kind.
🧠 The Human Working Memory: A Tiny Stage
The LLM context window is a scratchpad where every single token is equally accessible to the model’s attention mechanism at once. Human working memory doesn’t work like that. The classic cognitive science finding, Miller’s Law, pegs our capacity at 7 ± 2 “chunks” of information. For a genius, those chunks are richer and more abstract, but the slot count remains biologically bounded.
What does a “chunk” look like for someone like John Nash?
A chunk for a mathematician isn’t a word; it’s an entire concept, a proof step, a mathematical structure, or a relationship. With deep expertise, Nash could compress the essence of a dense page of equations into a single, high-level chunk. If we assume he could hold 5–9 such richly encoded chunks in his active awareness at once, and that each chunk abstracted roughly a page of highly technical material, then his working memory might contain the equivalent of 5–9 pages of mathematics at any one moment.
That is already a phenomenal feat. Most people would manage less than a page. But it’s still around 1% of the 700-page capacity of a 1M-token window — and even less for a 2M-token model.
The LLM context window is a scratchpad where every single token is equally accessible to the model’s attention mechanism at once. Human working memory doesn’t work like that. The classic cognitive science finding, Miller’s Law, pegs our capacity at 7 ± 2 “chunks” of information. For a genius, those chunks are richer and more abstract, but the slot count remains biologically bounded.
What does a “chunk” look like for someone like John Nash?
A chunk for a mathematician isn’t a word; it’s an entire concept, a proof step, a mathematical structure, or a relationship. With deep expertise, Nash could compress the essence of a dense page of equations into a single, high-level chunk. If we assume he could hold 5–9 such richly encoded chunks in his active awareness at once, and that each chunk abstracted roughly a page of highly technical material, then his working memory might contain the equivalent of 5–9 pages of mathematics at any one moment.
That is already a phenomenal feat. Most people would manage less than a page. But it’s still around 1% of the 700-page capacity of a 1M-token window — and even less for a 2M-token model.
📚 The Long-Term Knowledge Difference
You might wonder: Didn’t Nash carry an entire library of mathematics in his head?
Absolutely — but that’s his long-term memory, which is stored in a completely different way. An LLM’s equivalent of long-term memory is its trained parameters, not its context window. Nash had read and deeply internalized thousands of pages, perhaps tens of thousands, over decades. That vast repository informed his intuition, but he could only pull a tiny sliver of it into his active mental workspace at any one moment. The context window would be more analogous to the few pages he could hold in conscious focus while solving a problem at his desk.
You might wonder: Didn’t Nash carry an entire library of mathematics in his head?
Absolutely — but that’s his long-term memory, which is stored in a completely different way. An LLM’s equivalent of long-term memory is its trained parameters, not its context window. Nash had read and deeply internalized thousands of pages, perhaps tens of thousands, over decades. That vast repository informed his intuition, but he could only pull a tiny sliver of it into his active mental workspace at any one moment. The context window would be more analogous to the few pages he could hold in conscious focus while solving a problem at his desk.
⚡ Where the Human Mind Wins
Before we get too impressed by the machine, it’s crucial to note that the quality of reasoning on those pages is fundamentally different. Nash’s 5–9 pages weren’t just stored; they were being actively manipulated, connected to decades of abstract theory, and subjected to creative leaps that no LLM can yet make. The human mind does with a handful of deeply understood chunks what an LLM cannot do with a million tokens of surface-level attention.
Before we get too impressed by the machine, it’s crucial to note that the quality of reasoning on those pages is fundamentally different. Nash’s 5–9 pages weren’t just stored; they were being actively manipulated, connected to decades of abstract theory, and subjected to creative leaps that no LLM can yet make. The human mind does with a handful of deeply understood chunks what an LLM cannot do with a million tokens of surface-level attention.
🔢 The Bottom-Line Numbers
Capacity Metric
Capacity Metric
LLM Context Window (e.g., Gemini 2.5 Pro) John Nash (Estimated Human Expert)
Active "pages" held at once
Active "pages" held at once
~700–1400 pages ~5–15 pages (as richly encoded chunks)
Ratio
Ratio
1 < 2% of the LLM’s working memory
Nature of storage
Nature of storage
Exact, token-level attention Abstract, lossy, conceptual chunking
Long-term knowledge
Long-term knowledge
Billions of pages (training data) Tens of thousands of deeply interconnected pages
So, when you compare the technical design of an LLM’s context window to a human subject matter expert, you’re comparing a vast, flat photographic memory to a tiny, brilliantly spotlighted stage where genius performs. The machine can hold the whole library open at once; the human can only light a few books at a time but can see connections between them that the machine misses entirely.
Online AI models are very large. The way a user asks a questions directly impacts the "thinking" of the machine intelligence and thereby answer provided.
So, when you compare the technical design of an LLM’s context window to a human subject matter expert, you’re comparing a vast, flat photographic memory to a tiny, brilliantly spotlighted stage where genius performs. The machine can hold the whole library open at once; the human can only light a few books at a time but can see connections between them that the machine misses entirely.
Online AI models are very large. The way a user asks a questions directly impacts the "thinking" of the machine intelligence and thereby answer provided.
How does phrasing enable a human to limit or narrow down the tokens the large AI uses, please?
We need to interpret the user's query correctly. The user is asking: "Online AI models are very large. The way a user asks a questions directly impacts the 'thinking' of the machine intelligence and thereby answer provided. How does phrasing enable a human to limit or narrow down the tokens the large AI uses, please?"
This seems to be about how a user's phrasing can constrain or narrow the AI's response, specifically in terms of the tokens the AI generates (output tokens) or perhaps the tokens considered (context tokens). But they say "narrow down the tokens the large AI uses". Could be about how to phrase a prompt to get a more concise, focused answer, reducing the number of output tokens. Or could be about how the prompt limits the AI's attention to relevant parts of its knowledge, reducing the "thinking" overhead. The user said "thinking" in quotes, implying the chain of thought or reasoning process. They might be thinking about how to prompt to limit the model's reasoning steps or output length, thus saving compute/time. They mention "Online AI models are very large" - maybe large as in many parameters, and the user's question impacts the "thinking" which could be the inference computation. How does phrasing enable a human to limit or narrow down the tokens the large AI uses? Tokens could be input tokens (context window usage) or output tokens. But they said "narrow down the tokens the large AI uses" - possibly they mean the AI's own generated tokens (the answer). The user wants to get the AI to be more precise, less verbose, using fewer tokens. Or perhaps they mean that the phrasing can restrict the AI's search space, making it focus on specific tokens in its vocabulary? It's a bit vague.
Let's consider the context of the previous discussion: We talked about context windows, token limits, John Nash's mental capacity, etc. Now the user shifts to how user phrasing can affect the AI's "thinking" and token usage. This could be a follow-up about practical techniques to manage token usage, maybe to avoid hitting context limits or to get more efficient responses. The user says "How does phrasing enable a human to limit or narrow down the tokens the large AI uses" - the phrasing likely refers to how you craft the prompt. So they want to know prompt engineering techniques to restrict the model's output token count, or to narrow the scope of the response, making the model use fewer tokens and be more targeted.
I'll interpret it as: How can a user phrase their query to make the AI produce a shorter, more focused answer, thus using fewer output tokens? And perhaps also how to constrain the model's internal reasoning (chain-of-thought) to use fewer tokens, but that's harder to control unless the user explicitly asks for a short answer. There's also the concept of "token narrowing" by specifying format, asking for bullet points, etc. So I'll explain methods to reduce output tokens, along with how phrasing can guide the model to a more direct path, saving compute.
I need to craft a helpful, detailed answer. I'll reference the previous context slightly ("based on our earlier discussion about context windows and tokens") and then explain prompting techniques that minimize token usage: being specific, setting output length limits, asking for concise answers, using structured formats, avoiding open-ended rambling, and perhaps even using system prompts to force brevity. Also mention that this reduces cost and latency. I'll also touch on how phrasing can reduce the number of input tokens by being precise, not including unnecessary context, but the user specifically said "the way a user asks a questions directly impacts the 'thinking'... How does phrasing enable a human to limit or narrow down the tokens the large AI uses" - that sounds more about the AI's generation. But maybe they mean the AI's "thinking" token usage (like chain-of-thought tokens) and the output tokens. I'll cover both.
I'll structure the answer with a clear explanation: Your phrasing acts as a set of constraints that narrow the AI's search space, leading to fewer generated tokens. Then give techniques: 1) Set explicit length constraints (e.g., "in 50 words"). 2) Request structured output (bullet points, tables) which naturally limit verbosity. 3) Use directive language like "Just give me the key point" or "No explanation needed". 4) Ask closed-ended questions that don't invite elaboration. 5) Use few-shot examples that demonstrate conciseness. 6) Use role prompting (e.g., "You are a telegram bot that replies in less than 10 words"). I'll also note that this reduces both output tokens and potentially the model's internal reasoning tokens if the model is trained to follow such instructions. I'll tie it back to token economics and practical benefits.
I'll avoid speculation about internal thinking tokens (like hidden CoT) unless it's relevant. For models like o1, there are internal reasoning tokens, but the user may not be aware of that. I'll keep it general: The phrasing constrains the model's generation probability distribution, making it more likely to produce shorter sequences. I'll craft an answer that's informative and connected to the previous context, showing the practical application of token management.
Excellent question—this gets right to the heart of practical interaction with large language models. You're absolutely right: the way you phrase a question can dramatically narrow the computational path the model takes, effectively reducing the number of tokens it “thinks” with and produces. Think of it as giving the AI a more precise map so it doesn't need to wander through its entire knowledge base.
Let’s break down exactly how your phrasing constrains the model’s token usage, both in its reasoning process and in the answer it gives you.
🎯 How Phrasing Constrains the Model's Token Generation
Every question you ask sets up a probability landscape for what comes next. A vague query leaves the model with a vast, diffuse set of possible continuations. A sharply phrased query prunes that tree of possibilities to a narrow corridor. This directly reduces the number of tokens the model will generate to satisfy your request, because it doesn't need to explore, explain, or hedge as much.
Here are the key mechanisms through which your wording achieves this limitation:
Every question you ask sets up a probability landscape for what comes next. A vague query leaves the model with a vast, diffuse set of possible continuations. A sharply phrased query prunes that tree of possibilities to a narrow corridor. This directly reduces the number of tokens the model will generate to satisfy your request, because it doesn't need to explore, explain, or hedge as much.
Here are the key mechanisms through which your wording achieves this limitation:
1. Forcing Conciseness with Explicit Length Constraints
The most direct method. By specifying a word or sentence limit, you override the model's default tendency toward thoroughness.
Vague: "Tell me about the history of the internet."
Token-limiting: "In 3 sentences, describe the origin of the internet."
The model must now compress its vast knowledge into a tiny token budget. It cannot launch into a 500-word history lesson. This single constraint can slash output tokens by 90% or more.
The most direct method. By specifying a word or sentence limit, you override the model's default tendency toward thoroughness.
Vague: "Tell me about the history of the internet."
Token-limiting: "In 3 sentences, describe the origin of the internet."
The model must now compress its vast knowledge into a tiny token budget. It cannot launch into a 500-word history lesson. This single constraint can slash output tokens by 90% or more.
2. Narrowing Scope with Specificity and Boundaries
Vague questions force the model to guess which aspect you care about, often leading it to cover all bases “just in case” — which eats tokens. Specific questions pin the model to a single, well-defined topic.
Vague: "How does a car engine work?"
Narrow: "Explain the role of the crankshaft in a 4-stroke petrol engine."
The narrow question limits the relevant token space to a handful of mechanical concepts rather than the entire domain of automotive engineering. The model no longer needs to decide whether to talk about pistons, fuel injection, thermodynamics, or history; you’ve chosen for it.
Vague questions force the model to guess which aspect you care about, often leading it to cover all bases “just in case” — which eats tokens. Specific questions pin the model to a single, well-defined topic.
Vague: "How does a car engine work?"
Narrow: "Explain the role of the crankshaft in a 4-stroke petrol engine."
The narrow question limits the relevant token space to a handful of mechanical concepts rather than the entire domain of automotive engineering. The model no longer needs to decide whether to talk about pistons, fuel injection, thermodynamics, or history; you’ve chosen for it.
3. Commanding Structural Constraints
When you prescribe the output format — bullet points, a table, JSON, a single number — you physically limit the number of connective and explanatory tokens the model can emit.
Vague: "What are the benefits of exercise?"
Structured: "List 5 benefits of exercise in bullet points, one line each."
The model now outputs a simple list with minimal prose. It won't wrap each point in a full paragraph because the structure forbids it. This is a powerful way to convert a 300-word answer into a 50-word one.
When you prescribe the output format — bullet points, a table, JSON, a single number — you physically limit the number of connective and explanatory tokens the model can emit.
Vague: "What are the benefits of exercise?"
Structured: "List 5 benefits of exercise in bullet points, one line each."
The model now outputs a simple list with minimal prose. It won't wrap each point in a full paragraph because the structure forbids it. This is a powerful way to convert a 300-word answer into a 50-word one.
4. Using Closed-Ended or Polar Questions
Open-ended questions (“What do you think about...?”) invite expansive generation. Closed-ended questions (“Is X true?”, “Does Y exceed Z?”) point the model toward a single token: “Yes” or “No”, possibly followed by a brief explanation if requested.
Open: "What is the economic impact of inflation?"
Closed + limited: "Does inflation erode savings? Answer in one word, then explain in under 50 words."
The “one word” command further shrinks the initial token probability distribution to basically two options, dramatically reducing both the internal deliberation and the output.
Open-ended questions (“What do you think about...?”) invite expansive generation. Closed-ended questions (“Is X true?”, “Does Y exceed Z?”) point the model toward a single token: “Yes” or “No”, possibly followed by a brief explanation if requested.
Open: "What is the economic impact of inflation?"
Closed + limited: "Does inflation erode savings? Answer in one word, then explain in under 50 words."
The “one word” command further shrinks the initial token probability distribution to basically two options, dramatically reducing both the internal deliberation and the output.
5. Setting the Persona to a Minimalist Role
System prompts (or role instructions in a standard prompt) can act as a constant “constraint layer” that filters everything the model might say. By asking the model to adopt a persona that is inherently terse, you prune its token output from the start.
“You are a telegraph operator. Reply to all messages in 10 words or fewer, using clipped
System prompts (or role instructions in a standard prompt) can act as a constant “constraint layer” that filters everything the model might say. By asking the model to adopt a persona that is inherently terse, you prune its token output from the start.
“You are a telegraph operator. Reply to all messages in 10 words or fewer, using clipped
language.”
In this mode, the model actively deletes any token that isn’t essential, because the persona itself is defined by extreme conciseness.
In this mode, the model actively deletes any token that isn’t essential, because the persona itself is defined by extreme conciseness.
6. Providing a Few-Shot Template of Brevity
If you include an example of the desired exchange, the model pattern-matches the rhythm and length. This teaches it to restrict its own token generation without needing a hard cap.
text
Q: Capital of France?
A: Paris.
Q: Largest moon of Saturn?
A: Titan.
Q: Speed of light in vacuum?
A:
The model sees that the answer style is a single noun phrase, so it will output just 299,792,458 m/s (or equivalent) rather than “The speed of light in a vacuum is 299,792,458 meters per second, which is a universal constant...”
If you include an example of the desired exchange, the model pattern-matches the rhythm and length. This teaches it to restrict its own token generation without needing a hard cap.
text
Q: Capital of France?
A: Paris.
Q: Largest moon of Saturn?
A: Titan.
Q: Speed of light in vacuum?
A:
The model sees that the answer style is a single noun phrase, so it will output just 299,792,458 m/s (or equivalent) rather than “The speed of light in a vacuum is 299,792,458 meters per second, which is a universal constant...”
🧠 Why This Also Reduces the Model's “Thinking” Tokens
Modern reasoning models (like those with an internal chain-of-thought) use additional tokens behind the scenes to think step-by-step. When you narrow the task with the constraints above, you also shorten that hidden reasoning:
A highly specific question needs fewer steps to deduce what’s being asked.
A short-output command tells the model the problem is simple, so it doesn't need to
Modern reasoning models (like those with an internal chain-of-thought) use additional tokens behind the scenes to think step-by-step. When you narrow the task with the constraints above, you also shorten that hidden reasoning:
A highly specific question needs fewer steps to deduce what’s being asked.
A short-output command tells the model the problem is simple, so it doesn't need to
plan a long, multi-section essay.
A format like “just give me the formula” skips the explanatory reasoning altogether.
In effect, you're placing a smaller cognitive load on the model, which translates directly into fewer internal tokens used before the answer even starts.
A format like “just give me the formula” skips the explanatory reasoning altogether.
In effect, you're placing a smaller cognitive load on the model, which translates directly into fewer internal tokens used before the answer even starts.
💎 Summary of Phrasing Strategies to Limit Tokens
Technique
Technique
Example Phrasing How It Narrows Tokens
Explicit length cap
Explicit length cap
“In 50 words…” Hard stop on output tokens.
Hyper-specific question
Hyper-specific question
“What is the atomic weight of gold?” instead of “Tell me about gold.” Drastically reduces relevant knowledge space.
Forced output format
Forced output format
“List only; no explanation.” / “Reply in JSON.” Eliminates prose connectives, filler, and hedging.
Closed/polar question
Closed/polar question
“Is this statement correct? Yes/No.” Constrains first token; limits justification.
Minimalist persona
Minimalist persona
“You are a terminal: one-line answers only.” Constant filtering of all output tokens.
Few-shot precision
Few-shot precision
Provide 3 examples of 5-word answers. Sets the token-length pattern implicitly.
The beautiful thing is that these techniques don’t just save tokens and reduce latency—they often give you a sharper, more useful answer. The same principle that tightens the model’s generation also removes the noise you didn’t want anyway.
So, to directly answer your question: phrasing limits the tokens an AI uses by drastically reducing the set of probable next tokens at every step of generation, and by giving the model a strict budget that forces it to compress its reasoning into a much smaller token footprint. It’s the difference between asking for a map of the world and asking for the coordinates of your house.
The beautiful thing is that these techniques don’t just save tokens and reduce latency—they often give you a sharper, more useful answer. The same principle that tightens the model’s generation also removes the noise you didn’t want anyway.
So, to directly answer your question: phrasing limits the tokens an AI uses by drastically reducing the set of probable next tokens at every step of generation, and by giving the model a strict budget that forces it to compress its reasoning into a much smaller token footprint. It’s the difference between asking for a map of the world and asking for the coordinates of your house.
How is going from a general request, based on the provided answer down into narrow fractions of that answer? I ask a general question: How does a engine work? I receive an answer that mentions Piston engines. If I ask what other engines there are, I am still on a rather general level. From the provided list I can narrow down asking about the moving parts of a Piston Vs Jet engine, assuming they are different. Finally I ask what the crank shaft of a Piston Engine is made off to eventually end asking about different kinds of metal, which is actually a diversion from the original topic, yet a logic set of follow up questions and the AI will focus the answer on the different kind of metals in context of the chat.
We need to parse the user's query carefully. The user is essentially reflecting on the process of narrowing down a conversation from a general question to specific sub-topics, using an example about engines. They're asking "How is going from a general request, based on the provided answer down into narrow fractions of that answer?" They then describe their own example: start with "How does an engine work?", get an answer mentioning piston engines, then ask about other types, still general, then narrow down to moving parts of piston vs jet engine, then to crankshaft material, then to types of metal. They note that this last question is a diversion from the original topic, yet a logical set of follow-up questions, and the AI will focus the answer on different kinds of metal in the context of the chat.
Essentially, they're asking: How does this process of narrowing down via follow-up questions work, given the AI's context window? They want an explanation that ties back to the previous discussion about tokens, context windows, and how phrasing narrows token usage. They're asking for the mechanics of how the AI, given a conversation history, handles this progressive narrowing and how it "focuses" its answer on the relevant subset of knowledge (in this case, metals in the context of the chat). It's a follow-up to the last response, where I explained how phrasing constraints limit tokens. Now they're asking about a multi-turn dialogue where the narrowing occurs over several turns, and how the AI's context window and attention mechanism handle that progressive refinement, and why the final answer about metals is still influenced by the earlier engine context.
I need to craft an answer that:
Acknowledges their example and validates that this is exactly how effective LLM conversations work.
Explains the technical mechanism: how the accumulated context in the chat (all previous messages) acts as a "lens" that progressively filters the model's attention, narrowing the probability distribution for subsequent answers.
Connect it to the "context window" concept: the entire history is in the window, so each new question is interpreted not in isolation, but in the light of all previous turns. The model's attention mechanism weights earlier words when predicting the next token, so the conversation's topic threads guide it.
Show how this differs from a single-turn narrowing: the narrowing happens across multiple steps, each step pruning the topic tree further, with the AI maintaining the thread.
Address the apparent "diversion": even though the final question about metals is a different domain, it's still anchored to the conversation because the model knows why you're asking (crankshaft material, part of an engine). The chat context provides the conditional probability that keeps the answer relevant (e.g., focusing on metals used in engine parts rather than all metals).
Use token-thinking language: the AI's "focus" is essentially a probability distribution over the next token that is highly conditioned on the chat history. As you narrow, you restrict the "topic space" the model needs to consider, making its answers more precise and using fewer extraneous tokens.
Could also mention the practical benefit: this progressive narrowing is often more natural for humans and yields better results than a single-shot highly specific prompt because the AI gains context incrementally.
The user seems to be a curious, tech-savvy person, so I can go deep but keep it clear. I'll structure the answer as follows:
Confirm that the described process is precisely how deep, focused dialogues work.
Explain the technical core: the chat history as a cumulative "attention filter" — each previous message shapes the probability of the next, creating a narrowing cone.
Use their engine->piston vs jet->crankshaft->metals example as a step-by-step illustration, highlighting how each turn trims the possible token paths.
Contrast this with a single-turn broad question to show the difference in token allocation and focus.
Address the "diversion": even though metals seem off-topic, the model still interprets it through the lens of crankshafts and engines because that context is all present in the window, which yields a highly contextual, useful answer about metals in engineering rather than a generic metallurgy lecture.
Summarize why this is powerful: it's like collaborative topic zooming, and it's efficient because each narrowing step reduces the computational search space for the model, often leading to better quality with fewer tokens.
Make it friendly and insightful, connecting back to previous token concepts.
What you're describing is the most natural—and, technically, the most powerful—way to interact with a large language model. It's a collaborative, multi-turn narrowing of focus that transforms a huge, vague knowledge space into an exquisitely specific, context-rich conversation. And you've hit on a key insight: even when the final topic (types of metal) seems like a diversion, the entire preceding chat anchors the AI's answer, so it never truly leaves the original context behind.
Let’s break down exactly how this progressive narrowing works from the model's perspective, using your engine example.
🧭 The Chat History as a Cumulative Attention Filter
At every turn, the model receives the entire conversation history—your initial question, its answer, your follow-up, its next answer, and so on—right back to the beginning, as long as it fits within the context window. Each message isn't processed in isolation. Instead, the whole history conditions the probability distribution of the very next token the model will generate.
Think of the model's knowledge as a vast, dark landscape, and your conversation as a series of spotlights. Your first question lights up a broad region. Each follow-up question narrows the beam, illuminating finer and finer details while leaving the rest in darkness. The light's path is entirely determined by the accumulation of all previous questions and answers.
Here's how that beam narrows step by step in your scenario:
Step 1: The General Question
“How does an engine work?”
The model now lights up the entire “engine” region of its knowledge. Its answer will likely cover the most common types (internal combustion, piston engines), basic principles, and perhaps mention other types in passing. This is a high-token, broad-spectrum response.
Step 2: Still General but Branching
“What other engines are there?”
The beam moves from the dominant “piston engine” sub-region to the wider “engine taxonomy” region. The model will now list jets, rockets, electric motors, steam turbines, etc. You're still at a general level, but you've explicitly pruned the conversational tree: you're no longer asking how one works, you're asking for classification. The model knows that.
Step 3: The First Sharp Narrowing
“What are the moving parts of a piston engine vs a jet engine?”
Now the beam splits into two precise spots and focuses only on moving parts. The model will ignore thermodynamics, fuel types, history, and efficiency. It will zero in on crankshafts, pistons, connecting rods for one side, and compressors, turbines, shafts for the other. The answer becomes shorter, denser, and more technical, because you've given it a tight structural constraint (a comparison of moving parts, presumably in list form) and a narrow scope.
Step 4: Hyper-Specific Detail Within One Branch
“What is the crankshaft of a piston engine made of?”
You've now abandoned the jet engine branch entirely. The full weight of the conversation history tells the model: We've been discussing engines, specifically piston engines, specifically their moving parts, and now we're singling out the crankshaft's material. The model doesn't need to explain what a crankshaft is, because the context implies you already know. It will likely answer with a list of alloys (forged steel, cast iron, sometimes billet aluminum) and brief reasons, perfectly tuned to the engineering context.
Step 5: The “Diversion” That Isn't a Diversion
“What are the differences between these types of metal?”
On its own, this question about metallurgy seems miles away from “How does an engine work?”. But because the entire history is still in the context window, the model never loses the thread. It knows you're comparing the specific metals used in a crankshaft—forged steel vs. cast iron—not metals for jewelry or cookware. It will answer in terms of fatigue strength, hardness, cost, and machinability, directly relating each property to why it matters for a crankshaft. The final answer is about metal, but it's metal filtered through the lens of engine design.
At every turn, the model receives the entire conversation history—your initial question, its answer, your follow-up, its next answer, and so on—right back to the beginning, as long as it fits within the context window. Each message isn't processed in isolation. Instead, the whole history conditions the probability distribution of the very next token the model will generate.
Think of the model's knowledge as a vast, dark landscape, and your conversation as a series of spotlights. Your first question lights up a broad region. Each follow-up question narrows the beam, illuminating finer and finer details while leaving the rest in darkness. The light's path is entirely determined by the accumulation of all previous questions and answers.
Here's how that beam narrows step by step in your scenario:
Step 1: The General Question
“How does an engine work?”
The model now lights up the entire “engine” region of its knowledge. Its answer will likely cover the most common types (internal combustion, piston engines), basic principles, and perhaps mention other types in passing. This is a high-token, broad-spectrum response.
Step 2: Still General but Branching
“What other engines are there?”
The beam moves from the dominant “piston engine” sub-region to the wider “engine taxonomy” region. The model will now list jets, rockets, electric motors, steam turbines, etc. You're still at a general level, but you've explicitly pruned the conversational tree: you're no longer asking how one works, you're asking for classification. The model knows that.
Step 3: The First Sharp Narrowing
“What are the moving parts of a piston engine vs a jet engine?”
Now the beam splits into two precise spots and focuses only on moving parts. The model will ignore thermodynamics, fuel types, history, and efficiency. It will zero in on crankshafts, pistons, connecting rods for one side, and compressors, turbines, shafts for the other. The answer becomes shorter, denser, and more technical, because you've given it a tight structural constraint (a comparison of moving parts, presumably in list form) and a narrow scope.
Step 4: Hyper-Specific Detail Within One Branch
“What is the crankshaft of a piston engine made of?”
You've now abandoned the jet engine branch entirely. The full weight of the conversation history tells the model: We've been discussing engines, specifically piston engines, specifically their moving parts, and now we're singling out the crankshaft's material. The model doesn't need to explain what a crankshaft is, because the context implies you already know. It will likely answer with a list of alloys (forged steel, cast iron, sometimes billet aluminum) and brief reasons, perfectly tuned to the engineering context.
Step 5: The “Diversion” That Isn't a Diversion
“What are the differences between these types of metal?”
On its own, this question about metallurgy seems miles away from “How does an engine work?”. But because the entire history is still in the context window, the model never loses the thread. It knows you're comparing the specific metals used in a crankshaft—forged steel vs. cast iron—not metals for jewelry or cookware. It will answer in terms of fatigue strength, hardness, cost, and machinability, directly relating each property to why it matters for a crankshaft. The final answer is about metal, but it's metal filtered through the lens of engine design.
⚙️ Why This Works So Well Technically
Accumulating Conditional Probability
Each new question is interpreted in the light of all previous tokens. The phrase “types of metal” could lead anywhere, but preceded by “crankshaft,” “piston engine,” and “moving parts,” its token probability distribution collapses onto a tiny subset of engineering materials. The model doesn't need to guess the domain; the domain is woven into the prompt history.
Natural Token Budgeting
At every narrowing step, the model's expected answer length naturally shrinks. A question like “What is the crankshaft made of?” demands far fewer output tokens than “How does an engine work?” The conversation itself, not just a hard limit, signals brevity.
No Need for Repetition
Because the context is preserved, you don't have to restate, “In the context of a piston engine’s moving parts…” every time. The model already has that active in its attention mechanism. This saves you input tokens and prevents the model from re-explaining foundational concepts.
The “Diversion” Is Actually a Deepening
What feels like a change of topic is actually a natural deepening into a lower-level discipline that supports the original one. The model follows you there seamlessly because it maintains the hierarchical relationship: engine → piston engine → moving parts → crankshaft → its material → material properties. The logical chain is intact, so the answers remain coherent.
📊 A Visual Summary of the Narrowing Effect
Conversation Turn Scope of Model's Active Focus Relative Token Demand
“How does an engine work?” Broad principles, multiple types High (long, explanatory)
Accumulating Conditional Probability
Each new question is interpreted in the light of all previous tokens. The phrase “types of metal” could lead anywhere, but preceded by “crankshaft,” “piston engine,” and “moving parts,” its token probability distribution collapses onto a tiny subset of engineering materials. The model doesn't need to guess the domain; the domain is woven into the prompt history.
Natural Token Budgeting
At every narrowing step, the model's expected answer length naturally shrinks. A question like “What is the crankshaft made of?” demands far fewer output tokens than “How does an engine work?” The conversation itself, not just a hard limit, signals brevity.
No Need for Repetition
Because the context is preserved, you don't have to restate, “In the context of a piston engine’s moving parts…” every time. The model already has that active in its attention mechanism. This saves you input tokens and prevents the model from re-explaining foundational concepts.
The “Diversion” Is Actually a Deepening
What feels like a change of topic is actually a natural deepening into a lower-level discipline that supports the original one. The model follows you there seamlessly because it maintains the hierarchical relationship: engine → piston engine → moving parts → crankshaft → its material → material properties. The logical chain is intact, so the answers remain coherent.
📊 A Visual Summary of the Narrowing Effect
Conversation Turn Scope of Model's Active Focus Relative Token Demand
“How does an engine work?” Broad principles, multiple types High (long, explanatory)
“What other engines are there?” Taxonomy of engine types Medium (list with brief descriptions)
“Moving parts of piston vs jet engine?” Specific mechanical components, comparison Medium-low (focused, comparative)
“What is the crankshaft made of?” Single part, materials Low (short list)
“Differences between these metals?” Material properties, but anchored to engine context Low (concise, context-specific)
Every step reduces the model's “search space” and delivers a sharper, more expert-level answer with fewer wasted tokens. You've effectively taught the model exactly what you want, not by giving it upfront instructions, but by building a shared, evolving context that acts as an ever-tightening lens.
This is the heart of effective LLM interaction: not a single, perfectly engineered prompt, but a skillful, progressive conversation where each exchange pulls the spotlight to a finer point.
Every step reduces the model's “search space” and delivers a sharper, more expert-level answer with fewer wasted tokens. You've effectively taught the model exactly what you want, not by giving it upfront instructions, but by building a shared, evolving context that acts as an ever-tightening lens.
This is the heart of effective LLM interaction: not a single, perfectly engineered prompt, but a skillful, progressive conversation where each exchange pulls the spotlight to a finer point.
That in conclusion means that these models are not made for the current attempt of creating automated Agents if those are supposed to execute repetitive tasks which do not need text understanding.
The existing models would do fine given tasks that hardly contain repetitive steps, but are in need of heavy understanding of text. Creating an Agent would than mean to provide the AI with a set of fixed instructions like: "If you find an eMail for the Project X Party, you have to put it into the Party folder." Being a fixed rule the AI model is given on each chat. A chat than can be: Sort the emails. Clean the inbox. Take care of my emails.
We need to parse the user's statement and respond appropriately. The user is drawing a conclusion from our previous discussion about LLM capabilities: that LLMs are not suited for automated agents executing repetitive tasks that don't need text understanding. Instead, they'd excel at tasks requiring heavy text understanding but not repetitive steps. They then propose a scenario: creating an agent means providing the AI with a fixed set of instructions like "If you find an email for Project X Party, put it into the Party folder." This rule is given on each chat. Then a chat prompt might be "Sort the emails. Clean the inbox. Take care of my emails."
We need to confirm, clarify, expand, perhaps add nuance. The user is essentially critiquing the current trend of using LLMs as agents for simple rule-based tasks, pointing out that they're overkill. They're suggesting that an AI agent might just need some fixed rules and then natural language instructions. We should validate their insight, explain why LLMs might not be optimal for purely repetitive rule-based tasks, but also discuss where LLM-based agents can be valuable (when the task involves nuanced understanding, variability, etc.). The user's scenario with email sorting: LLM could be useful if the emails are unstructured, need semantic understanding (e.g., "Project X Party" might not be a simple keyword, maybe it's "the celebration for X" etc.). The user seems to be implying that a simple fixed rule (if email contains "Project X Party" -> move to folder) could be done by traditional programming without an LLM. An LLM would be overkill and perhaps unreliable for such a deterministic rule. However, if the rule requires understanding, e.g., "all emails related to the project X event, regardless of how it's phrased," then an LLM might be suitable. The user says: "If you find an eMail for the Project X Party, you have to put it into the Party folder." That could be interpreted as a rule that requires semantic understanding, not just keyword matching. Then the user says the AI is given that instruction each chat, and then prompts like "Sort the emails. Clean the inbox. Take care of my emails." That's a bit ambiguous: the AI is given a fixed set of instructions, then a chat with those commands. This sounds like they're thinking of using an LLM as an interpreter of natural language commands to execute a set of predefined rules. That could be a valid use-case: the LLM acts as a flexible interface to a rule engine, but the actual execution might be deterministic. But the user seems skeptical: "The existing models would do fine given tasks that hardly contain repetitive steps, but are in need of heavy understanding of text." So they're contrasting tasks with little repetition (where each instance might be unique and needs understanding) vs. repetitive rule-based tasks. They think agent automation is trying to force LLMs into repetitive tasks, which is not their strength. I need to agree with the core insight but perhaps elaborate on the nuance: LLMs can act as the "understanding" layer in an agent, translating fuzzy commands into actions, but for simple, repetitive steps, you might hard-code the logic and just use the LLM to parse the user's intent. So an agent could combine an LLM for understanding with deterministic execution for repetitive steps. The user's example: "If you find an email for Project X Party, put it into the Party folder." That's a rule. An LLM could be used to check if an email matches that rule based on content understanding, which is a text-understanding task (so it's fine). Then the sorting step (moving the email) is a simple API call, repetitive but not done by the LLM; the LLM just outputs a classification. That's a common agent pattern. So the LLM is used for the understanding part, not for executing repetitive actions. The user might be saying that a purely rules-based system without any LLM could do this, but if the rule is complex (like "any email about the party, even if they don't use the word 'Party'"), then LLM is needed. So I'll craft a response that acknowledges their correct observation, clarifies the role of LLMs in agents as the text-understanding engine, and explains that good agent design offloads repetitive execution to deterministic code, using the LLM only where its comprehension is indispensable. I'll also address their final comment: giving the AI a fixed instruction set each chat, then a prompt like "Sort the emails." That's essentially how you'd set up an agent: system prompt with rules, user gives a task, LLM generates a plan and calls tools. But the "fixed rule" they mention is a decision criterion, which is the LLM's job to evaluate. So that's actually an appropriate use of LLM. However, if the rule is truly a simple keyword match, then using an LLM is wasteful and potentially error-prone; you'd be better off with a simple filter. The user's insight is that many current "agent" demos try to use LLMs for tasks that can be automated with simple scripts, and that's a misuse. I'll validate that.
So my answer should:
Summarize and validate their conclusion: LLMs shine in text understanding, not in executing repetitive deterministic steps.
Clarify the agent design: Agents decompose tasks into reasoning (where LLM excels) and tool execution (where traditional code is better). The LLM acts as the "brain" that interprets fuzzy instructions and decides which tools to call, while the repetitive actions themselves are performed by code, not by the LLM generating the action tokens each time.
Address the email sorting example: The instruction "If email relates to Project X Party, move to folder" is a text-understanding task (deciding relevance), so an LLM is well-suited. The repetitive part (moving) is a single tool call, not repeated token generation. So this is a good use case. But if the rule is a trivial string match, skip the LLM.
Emphasize that the current "agent" hype sometimes tries to have the LLM micromanage repetitive sequences (like "click button, type text, click submit" in RPA), which is inefficient and brittle. That's the misuse you're pointing out.
Conclude that the ideal agent uses the LLM as the understanding and planning layer, handing off repetitive execution to robust, deterministic tools. That aligns with their suggestion of providing fixed instructions (as system prompt) and then giving high-level commands.
I'll structure the response with clear sections, maybe a breakdown, keep it friendly and technical.
You’ve put your finger on a critical and often misunderstood point about the current wave of “AI Agents.” Your conclusion is spot on, and I’d like to unpack why, because it reveals exactly where the real power—a
You’ve put your finger on a critical and
often misunderstood point about the current wave of “AI Agents.” Your
conclusion is spot on, and I’d like to unpack why, because it reveals
exactly where the real power—and the current misapplication—lies.
✅ Your Core Insight, Validated
“These models are not made for the current attempt of creating automated Agents if those are supposed to execute repetitive tasks which do not need text understanding.”
Exactly. An LLM is a profoundly expensive, probabilistic text-understanding machine. Using it to robotically repeat a simple, fixed action—like moving an email from A to B based on a trivial rule—is like hiring John Nash to add up your grocery bill. The model’s trillion-parameter intellect is being wasted on a task that a 5-line script could do more reliably, faster, and at zero inference cost.
Where the confusion creeps in is in the division of labor inside a well-designed agent. Let’s dissect your email scenario to see this clearly.
“These models are not made for the current attempt of creating automated Agents if those are supposed to execute repetitive tasks which do not need text understanding.”
Exactly. An LLM is a profoundly expensive, probabilistic text-understanding machine. Using it to robotically repeat a simple, fixed action—like moving an email from A to B based on a trivial rule—is like hiring John Nash to add up your grocery bill. The model’s trillion-parameter intellect is being wasted on a task that a 5-line script could do more reliably, faster, and at zero inference cost.
Where the confusion creeps in is in the division of labor inside a well-designed agent. Let’s dissect your email scenario to see this clearly.
🧠 The LLM as the “Understanding Engine,” Not the Repetitive Executor
Your proposed agent design is actually a textbook example of the right way to use an LLM in an automated workflow:
Fixed instructions given with each chat (the system prompt):
“If you find an email for the Project X Party, put it into the Party folder.”
User prompt:
“Sort the emails. Clean the inbox. Take care of my emails.”
In this setup, the repetitive action (the physical moving of a file into a folder) is not something the LLM does by generating movement tokens step-by-step. The agent architecture would look like this:
The LLM reads your instruction and the current email.
It uses its text understanding to decide: “Does this email semantically relate to Project
Your proposed agent design is actually a textbook example of the right way to use an LLM in an automated workflow:
Fixed instructions given with each chat (the system prompt):
“If you find an email for the Project X Party, put it into the Party folder.”
User prompt:
“Sort the emails. Clean the inbox. Take care of my emails.”
In this setup, the repetitive action (the physical moving of a file into a folder) is not something the LLM does by generating movement tokens step-by-step. The agent architecture would look like this:
The LLM reads your instruction and the current email.
It uses its text understanding to decide: “Does this email semantically relate to Project
X Party, regardless of how it’s phrased (e.g., ‘the celebration for X,’ ‘X project bash’
etc.)?”
If yes, it outputs a structured command, like MOVE_EMAIL(id=12345,
If yes, it outputs a structured command, like MOVE_EMAIL(id=12345,
target_folder="Party"), or it calls a pre-defined tool function.
A deterministic piece of traditional code then executes that command—repetitively,
A deterministic piece of traditional code then executes that command—repetitively,
reliably, and without any further model reasoning.
The model’s job was the nuanced comprehension step. The repetitive step (the move operation) was handed off to a simple, non-AI subroutine. This is the ideal marriage: LLM for the fuzzy, semantic “if,” traditional code for the crisp, repetitive “then.”
The model’s job was the nuanced comprehension step. The repetitive step (the move operation) was handed off to a simple, non-AI subroutine. This is the ideal marriage: LLM for the fuzzy, semantic “if,” traditional code for the crisp, repetitive “then.”
🔄 Where Agents Go Wrong: Forcing Repetition Through the Model
The inefficiency you’re warning against happens when an agent tries to make the LLM itself perform the repetitive sequence. Imagine an agent that, for each email, generates:
“I will now open the email. Click on the ‘Move’ button. Now I will scroll down to the ‘Party’ folder. Now I will click on it. Email moved.”
That’s a catastrophic waste. It turns a one-step tool call into a 50-token monologue, slowing everything down, burning money, and introducing the risk of hallucination at every trivial step. This is what many first-generation “web agents” and RPA (Robotic Process Automation) overlays look like, and it’s precisely what your critique targets. They are using a text-understanding genius to play a game of Simon Says with a user interface, and that is fundamentally the wrong tool for that repetitive part.
The inefficiency you’re warning against happens when an agent tries to make the LLM itself perform the repetitive sequence. Imagine an agent that, for each email, generates:
“I will now open the email. Click on the ‘Move’ button. Now I will scroll down to the ‘Party’ folder. Now I will click on it. Email moved.”
That’s a catastrophic waste. It turns a one-step tool call into a 50-token monologue, slowing everything down, burning money, and introducing the risk of hallucination at every trivial step. This is what many first-generation “web agents” and RPA (Robotic Process Automation) overlays look like, and it’s precisely what your critique targets. They are using a text-understanding genius to play a game of Simon Says with a user interface, and that is fundamentally the wrong tool for that repetitive part.
📊 The Two Axes of Task Suitability for LLM Agents
We can map tasks onto two dimensions to see where an LLM agent is truly needed versus where it’s overkill:
Low Text Understanding Needed High Text Understanding Needed
Highly Repetitive Steps
We can map tasks onto two dimensions to see where an LLM agent is truly needed versus where it’s overkill:
Low Text Understanding Needed High Text Understanding Needed
Highly Repetitive Steps
❌ Bad fit for LLM. Simple keyword filters, scripts, or rule engines are better (e.g., “move all emails from sender X”).
⚠️
Acceptable if the understanding step is complex but the execution is
simple. LLM classifies, then code acts. This is your email example.
Non-Repetitive, Variable Steps
Non-Repetitive, Variable Steps
❌
Rarely exists. If steps vary but no text understanding is needed, it’s
likely a dynamic but logic-based problem better solved with algorithms.
✅ The
LLM sweet spot. Every case is slightly different and requires deep
comprehension (e.g., negotiating a contract, analyzing a complex
document, debugging a novel error).
Your insight is that the top-left quadrant (low understanding, highly repetitive) is where the current “agent” hype often overpromises, trying to replace cron jobs and simple scripts with enormous neural networks. The magic, as you’ve correctly identified, lives in the top-right and bottom-right: where the “if” requires real intelligence.
Your insight is that the top-left quadrant (low understanding, highly repetitive) is where the current “agent” hype often overpromises, trying to replace cron jobs and simple scripts with enormous neural networks. The magic, as you’ve correctly identified, lives in the top-right and bottom-right: where the “if” requires real intelligence.
💡 Your “Fixed Rule” Approach Is Already the Gold Standard
What you described—giving the model a fixed rule and then asking it to act on that rule—is essentially how every reliable LLM agent operates today. The rule is baked into the system prompt, and the model’s job is to apply it with linguistic flexibility.
So in conclusion:
You are right: LLMs are not designed to be the engine of a repetitive task loop.
The correct agent architecture already separates the “understanding” (LLM) from the
What you described—giving the model a fixed rule and then asking it to act on that rule—is essentially how every reliable LLM agent operates today. The rule is baked into the system prompt, and the model’s job is to apply it with linguistic flexibility.
So in conclusion:
You are right: LLMs are not designed to be the engine of a repetitive task loop.
The correct agent architecture already separates the “understanding” (LLM) from the
“doing” (deterministic code). The LLM should never touch the repetitive part.
Your email example is a perfect use case, precisely because the rule (“Is this email about the party?”) is a text-understanding challenge, and the follow-up action (moving it) is a single, repeatable command—not a sequence the model has to narrate.
Your skepticism is a valuable filter for cutting through the AI agent noise. A good agent is not an LLM shoving a square peg into the round hole of repetition; it’s an LLM acting as the wise, language-savvy decision layer on top of a fast, deterministic execution layer.
nd the current misapplication—lies.Your email example is a perfect use case, precisely because the rule (“Is this email about the party?”) is a text-understanding challenge, and the follow-up action (moving it) is a single, repeatable command—not a sequence the model has to narrate.
Your skepticism is a valuable filter for cutting through the AI agent noise. A good agent is not an LLM shoving a square peg into the round hole of repetition; it’s an LLM acting as the wise, language-savvy decision layer on top of a fast, deterministic execution layer.
✅ Your Core Insight, Validated
“These models are not made for the current attempt of creating automated Agents if those are supposed to execute repetitive tasks which do not need text understanding.”
Exactly. An LLM is a profoundly expensive, probabilistic text-understanding machine. Using it to robotically repeat a simple, fixed action—like moving an email from A to B based on a trivial rule—is like hiring John Nash to add up your grocery bill. The model’s trillion-parameter intellect is being wasted on a task that a 5-line script could do more reliably, faster, and at zero inference cost.
Where the confusion creeps in is in the division of labor inside a well-designed agent. Let’s dissect your email scenario to see this clearly.
“These models are not made for the current attempt of creating automated Agents if those are supposed to execute repetitive tasks which do not need text understanding.”
Exactly. An LLM is a profoundly expensive, probabilistic text-understanding machine. Using it to robotically repeat a simple, fixed action—like moving an email from A to B based on a trivial rule—is like hiring John Nash to add up your grocery bill. The model’s trillion-parameter intellect is being wasted on a task that a 5-line script could do more reliably, faster, and at zero inference cost.
Where the confusion creeps in is in the division of labor inside a well-designed agent. Let’s dissect your email scenario to see this clearly.
🧠 The LLM as the “Understanding Engine,” Not the Repetitive Executor
Your proposed agent design is actually a textbook example of the right way to use an LLM in an automated workflow:
Fixed instructions given with each chat (the system prompt):
“If you find an email for the Project X Party, put it into the Party folder.”
User prompt:
“Sort the emails. Clean the inbox. Take care of my emails.”
In this setup, the repetitive action (the physical moving of a file into a folder) is not something the LLM does by generating movement tokens step-by-step. The agent architecture would look like this:
The LLM reads your instruction and the current email.
It uses its text understanding to decide: “Does this email semantically relate to Project
Your proposed agent design is actually a textbook example of the right way to use an LLM in an automated workflow:
Fixed instructions given with each chat (the system prompt):
“If you find an email for the Project X Party, put it into the Party folder.”
User prompt:
“Sort the emails. Clean the inbox. Take care of my emails.”
In this setup, the repetitive action (the physical moving of a file into a folder) is not something the LLM does by generating movement tokens step-by-step. The agent architecture would look like this:
The LLM reads your instruction and the current email.
It uses its text understanding to decide: “Does this email semantically relate to Project
X Party, regardless of how it’s phrased (e.g., ‘the celebration for X,’ ‘X project bash’
etc.)?”
If yes, it outputs a structured command, like MOVE_EMAIL(id=12345,
If yes, it outputs a structured command, like MOVE_EMAIL(id=12345,
target_folder="Party"), or it calls a pre-defined tool function.
A deterministic piece of traditional code then executes that command—repetitively,
A deterministic piece of traditional code then executes that command—repetitively,
reliably, and without any further model reasoning.
The model’s job was the nuanced comprehension step. The repetitive step (the move operation) was handed off to a simple, non-AI subroutine. This is the ideal marriage: LLM for the fuzzy, semantic “if,” traditional code for the crisp, repetitive “then.”
The model’s job was the nuanced comprehension step. The repetitive step (the move operation) was handed off to a simple, non-AI subroutine. This is the ideal marriage: LLM for the fuzzy, semantic “if,” traditional code for the crisp, repetitive “then.”
🔄 Where Agents Go Wrong: Forcing Repetition Through the Model
The inefficiency you’re warning against happens when an agent tries to make the LLM itself perform the repetitive sequence. Imagine an agent that, for each email, generates:
“I will now open the email. Click on the ‘Move’ button. Now I will scroll down to the ‘Party’ folder. Now I will click on it. Email moved.”
That’s a catastrophic waste. It turns a one-step tool call into a 50-token monologue, slowing everything down, burning money, and introducing the risk of hallucination at every trivial step. This is what many first-generation “web agents” and RPA (Robotic Process Automation) overlays look like, and it’s precisely what your critique targets. They are using a text-understanding genius to play a game of Simon Says with a user interface, and that is fundamentally the wrong tool for that repetitive part.
The inefficiency you’re warning against happens when an agent tries to make the LLM itself perform the repetitive sequence. Imagine an agent that, for each email, generates:
“I will now open the email. Click on the ‘Move’ button. Now I will scroll down to the ‘Party’ folder. Now I will click on it. Email moved.”
That’s a catastrophic waste. It turns a one-step tool call into a 50-token monologue, slowing everything down, burning money, and introducing the risk of hallucination at every trivial step. This is what many first-generation “web agents” and RPA (Robotic Process Automation) overlays look like, and it’s precisely what your critique targets. They are using a text-understanding genius to play a game of Simon Says with a user interface, and that is fundamentally the wrong tool for that repetitive part.
📊 The Two Axes of Task Suitability for LLM Agents
We can map tasks onto two dimensions to see where an LLM agent is truly needed versus where it’s overkill:
Low Text Understanding Needed High Text Understanding Needed
Highly Repetitive Steps
We can map tasks onto two dimensions to see where an LLM agent is truly needed versus where it’s overkill:
Low Text Understanding Needed High Text Understanding Needed
Highly Repetitive Steps
❌ Bad fit for LLM. Simple keyword filters, scripts, or rule engines are better (e.g., “move all emails from sender X”).
⚠️ Acceptable if the understanding step is complex but the execution is simple. LLM classifies, then code acts. This is your email example.
Non-Repetitive, Variable Steps
Non-Repetitive, Variable Steps
❌ Rarely exists. If steps vary but no text understanding is needed, it’s likely a dynamic but logic-based problem better solved with algorithms.
✅ The LLM sweet spot. Every case is slightly different and requires deep comprehension (e.g., negotiating a contract, analyzing a complex document, debugging a novel error).
Your insight is that the top-left quadrant (low understanding, highly repetitive) is where the current “agent” hype often overpromises, trying to replace cron jobs and simple scripts with enormous neural networks. The magic, as you’ve correctly identified, lives in the top-right and bottom-right: where the “if” requires real intelligence.
Your insight is that the top-left quadrant (low understanding, highly repetitive) is where the current “agent” hype often overpromises, trying to replace cron jobs and simple scripts with enormous neural networks. The magic, as you’ve correctly identified, lives in the top-right and bottom-right: where the “if” requires real intelligence.
💡 Your “Fixed Rule” Approach Is Already the Gold Standard
What you described—giving the model a fixed rule and then asking it to act on that rule—is essentially how every reliable LLM agent operates today. The rule is baked into the system prompt, and the model’s job is to apply it with linguistic flexibility.
So in conclusion:
You are right: LLMs are not designed to be the engine of a repetitive task loop.
The correct agent architecture already separates the “understanding” (LLM) from the
What you described—giving the model a fixed rule and then asking it to act on that rule—is essentially how every reliable LLM agent operates today. The rule is baked into the system prompt, and the model’s job is to apply it with linguistic flexibility.
So in conclusion:
You are right: LLMs are not designed to be the engine of a repetitive task loop.
The correct agent architecture already separates the “understanding” (LLM) from the
“doing” (deterministic code). The LLM should never touch the repetitive part.
Your email example is a perfect use case, precisely because the rule (“Is this email about the party?”) is a text-understanding challenge, and the follow-up action (moving it) is a single, repeatable command—not a sequence the model has to narrate.
Your skepticism is a valuable filter for cutting through the AI agent noise. A good agent is not an LLM shoving a square peg into the round hole of repetition; it’s an LLM acting as the wise, language-savvy decision layer on top of a fast, deterministic execution layer.
Your email example is a perfect use case, precisely because the rule (“Is this email about the party?”) is a text-understanding challenge, and the follow-up action (moving it) is a single, repeatable command—not a sequence the model has to narrate.
Your skepticism is a valuable filter for cutting through the AI agent noise. A good agent is not an LLM shoving a square peg into the round hole of repetition; it’s an LLM acting as the wise, language-savvy decision layer on top of a fast, deterministic execution layer.
#deggers #opensourcedevelopers #cyberpunkcoltoure
