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Keywords: ChatGPT, memory, LLM, user experience, RAG
Overview: This article analyzes how ChatGPT’s memory system works, differentiating it from other Large Language Models (LLMs). It explores the “Saved Memory” and “Chat History” systems, detailing the components of the latter: current session history, conversation history, and user insights. The author reverse-engineers the memory systems, infers technical implementations, and discusses the impact on user experience, concluding that user insights likely contribute most significantly to ChatGPT’s improved performance. The analysis includes experimental observations and potential implementation strategies for replicating ChatGPT’s memory capabilities.
Section Summaries:
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How ChatGPT’s Memory Works: ChatGPT’s memory is divided into “Saved Memory,” which is user-controlled, and “Chat History,” which is more complex. The Chat History system appears to be composed of current session history, conversation history, and user insights. These systems contribute significantly to improved assistant responses.
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Technical Implementation: The author attempts to recreate the observed behavior of ChatGPT’s memory systems. Saved memories can be approximated with a tool that accepts user messages and existing facts, returning new facts or a refusal. Reference chat history can be implemented using vector spaces indexed by conversation and message content.
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User Experience: ChatGPT’s memory systems enhance user experience compared to using the models through the API. Saved memory allows users to tailor responses, while user insights automate the memory process. Conversation history gives chatbots the context of past interactions, avoiding repetitive interactions.
Related Tools:
- Bio Tool (described for saving memories within ChatGPT)
References:
Original Article Link: https://macro.com/app/md/54115a42-3409-4f5b-9120-f144d3ecd23a
source: https://macro.com/app/md/54115a42-3409-4f5b-9120-f144d3ecd23a