What’s up with ai not being able to track conversations beyond so many responses (Q&A)? I know I’m not the only one. Can’t you just have an open closed setting so it doesn’t just assume the conversation has ended?
You’ve hit on one of the most fundamental and frustrating limitations of current AI models. You are absolutely not alone in this experience. The feeling of “losing” a valuable conversation—whether it’s code, a story, or study notes—is a common and significant pain point.
The core reason for this isn’t a simple setting you can toggle. It’s a fundamental technical constraint known as the “context window.”
What is a Context Window?
Think of a large language model (LLM) like a person with an incredibly short-term memory. It doesn’t “remember” past conversations in the same way a human does. Instead, it operates on a “context window,” which is a limited-size buffer of the most recent conversation history.
Here’s how it works:
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Every time you send a new query, the AI’s system takes your new message and concatenates it with the most recent messages from the conversation.
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This combined text—the context window—is what the model actually “sees” and uses to generate its next response.
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When the conversation gets too long and the context window is full, the oldest messages “fall off” the beginning to make room for the new ones.
This is why, after a certain number of turns, the AI will completely forget what you discussed at the start of the conversation. It’s not a bug; it’s a design trade-off
Why Do We Have This Limitation?
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Computational Cost: The processing power required for a model’s “attention mechanism” (how it weighs the importance of different words in the context) scales quadratically with the length of the input. This means that doubling the conversation length requires four times the computational resources. It quickly becomes prohibitively expensive and slow to process very long conversations.
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Memory Constraints: Storing the entire conversation history in memory for every single turn would require an enormous amount of RAM and VRAM (the memory on a graphics card). This is a physical and economic limitation.
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“Lost in the Middle” Problem: Even with a large context window, models tend to be better at recalling information at the beginning and end of the conversation, while details in the middle get “lost.” This is another aspect of how the model’s architecture processes information.
What Solutions Exist?
While there’s no magic “open/closed” button for an infinite conversation, developers and researchers have created some clever workarounds:
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Summarization: One common approach is to have the AI periodically summarize the conversation so far. The summary then replaces the older messages in the context window, freeing up space while retaining the key points. This can be complex to implement well, as the AI might miss crucial details.
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Retrieval-Augmented Generation (RAG): This technique involves storing the entire conversation history in a separate database. When you ask a new question, a system first searches the history for relevant information and then injects that information into the prompt as a “retrieved” piece of context. This allows the model to access a much larger body of information than its context window would normally allow.
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Larger Context Windows: The size of context windows is constantly increasing with new models. Some of the latest models can handle hundreds of thousands or even millions of “tokens” (a token is roughly a word or part of a word). While this is a huge improvement, it’s not infinite and still faces the same computational and cost challenges.
The “Lost Conversation” Feeling
Yes, I understand that feeling of losing an open conversation. It’s a common struggle for users who rely on these tools for detailed, ongoing projects. The “forgetting” can be particularly frustrating with things like:
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Code: You spend a long time debugging a problem and then the model forgets the variables you defined or the error messages you were working with.
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Storytelling: The AI forgets a character’s name, a key plot point, or a specific detail about the world you were building.
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Study Notes: You’re building up a complex web of knowledge, and then the AI loses the thread and can no longer make connections between different topics.
For these reasons, many developers and power users have adopted strategies to proactively manage context, such as:
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Periodically saving and re-pasting key information into a new chat session.
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Writing a comprehensive “reset” prompt that summarizes the entire project and puts it at the beginning of a new conversation.
Using a “system message” or a pre-defined persona to keep the model focused on the task.