ChatGPT Forgets. I’m Building Something That Helps It Remember.
One of the biggest challenges with current AI chatbots like ChatGPT is their short-term memory. We’ve all seen it – you have a detailed conversation with the AI, but as soon as you start a new chat, it’s like talking to a stranger again. ChatGPT does not retain information from past sessions unless enabled. Each conversation is essentially a blank slate. Imagine conversing with an AI that forgets everything the moment a chat window closes, versus one that can seamlessly reference your previous interactions – the difference would be huge . This lack of long-term memory is more than a minor inconvenience; it limits how “smart” and personal these AI assistants can feel.
The core limitation is still technical. ChatGPT’s responses are shaped by a context window, which holds a certain amount of recent conversation measured in tokens. In GPT-4o, this window has increased to 128,000 tokens, allowing the model to reference much longer conversations in a single session. However, this memory is temporary. Once a session ends, the entire conversation history is gone unless memory is enabled.
ChatGPT now includes a memory feature that can retain selected facts between sessions, such as your name, preferences, or recurring topics. This helps with personalization, but it does not store or recall detailed past conversations by default. When starting a new chat, the model does not automatically continue where you left off unless that information has been saved to memory. Even then, the continuity is limited. The current memory system is useful for general context but not yet reliable for deeper or project-specific recall across sessions.
Why Does AI Memory Matter?
Memory is the backbone of any intelligent conversation. For an AI to feel truly helpful (or even human-like), it needs to remember context, recall facts, and maintain consistency over time. In the sci-fi film Her, the AI assistant Samantha stands out because of her consistency and coherence. She remembers the protagonist’s jokes, references past conversations naturally, and asks relevant follow-up questions. Today’s LLMs, including ChatGPT, lag in this area . They can’t yet match Samantha’s level of long-term understanding because they lack persistent memory.
Why is having a better memory so important for AI?
Contextual Understanding: Memory lets the AI maintain coherent, relevant responses over a long dialogue. With persistent context, the AI won’t contradict itself or repeat questions you’ve already answered.
Personalization: If the AI remembers your past conversations, it can tailor its answers to you. It could recall your preferences, projects, or problems you discussed before. Without memory, every session feels generic and impersonal.
Efficiency: A good memory saves you from re-explaining things. You shouldn’t need to remind the chatbot of your name or recap last week’s discussion each time. Persistent memory would make interactions more seamless and human-like.
In short, memory is what can make an AI assistant feel less like a one-off tool and more like an ongoing partner. It’s essential for achieving the kind of natural, flowing conversations we imagine for advanced AI.
Current Limitations and Workarounds
Until we develop true long-term memory for AI, how do we cope with these limitations? There are a few strategies and partial solutions out there.
1. Manual Context Refresh: The simplest workaround is to manually remind the AI of important context at the start of each new chat. Many users do this by writing a quick summary of the last conversation when they begin a new one. This might include key facts, decisions, or any details the AI will need. By providing a concise “memory refresh” upfront, you help the model maintain continuity. It’s a bit like giving the AI a note saying “Previously, in our conversation…” each time you chat.
2. Summarizing and Injecting Memory: A more automated approach is to have the AI itself summarize the conversation as it goes, and then use that summary as context later. Some in the AI community suggest exporting the entire chat history and asking the LLM to condense it into key points, which can then be added into the system prompt of the next session. In other words, after a long discussion, you have ChatGPT summarize “what it should remember,” and next time you feed that summary back to it. This effectively gives the new session some recollection of the old one. It’s not true memory, but it mimics it by carrying over the important bits of context.
3. Larger Context Windows: Another partial solution is using models with much larger context windows. For example, Anthropic’s latest Claude models, including Claude Opus and Claude Sonnet, support context windows of up to 200,000 tokens in a single conversation. These larger windows allow the AI to retain information from much longer interactions without dropping earlier parts of the conversation. This is useful for tasks that require long-form reasoning, reviewing documents, or holding extended discussions.
However, the memory is still limited to the current session. When you start a new chat, the model does not automatically remember anything from previous ones unless separate memory features are used. These extended context windows help improve continuity within a session, but they do not solve the problem of long-term memory across sessions.
My Approach: Building a Memory Profile for ChatGPT
I’ve felt this memory limitation firsthand, and it inspired me to work on my own solution. My approach is to create what I call a “memory profile summary” for my chats. In simple terms, I’m developing a tool that will extract the key points from my past conversations with ChatGPT and compile them into a summary file. This summary acts like the AI’s memory of me, a profile of who I am, what I’m working on, what I like or dislike, and the important details from previous chats.
Here’s how it works at a high level: after I finish a chat session, the tool will go through the transcript and pick out the important information. For example, if over several chats I told the AI about my project ideas, my writing style preferences, or personal anecdotes, those would be noted. The tool distills all that into a concise synopsis of “things to remember about the user and ongoing topics.” Then, when I start a new chat, I can prepend this memory summary into the conversation (likely as part of the system message or initial user prompt). This way, ChatGPT isn’t truly remembering by itself, but it’s given a detailed cheat-sheet of our past interactions.
Crucially, this memory file will be persistent. I can expand it over time as new important details come up in conversation. It functions like a knowledge file that captures our dialogue history. The next time I open a chat, I don’t have to start from scratch. I can have the AI read the profile summary first. For example, if I discussed a book idea last month, the summary can remind the AI of the premise and characters we developed. From there, the conversation can continue more naturally, almost as if it remembered, similar to how a human would.
I’m keeping the implementation details light in this post to avoid going too technical, but the concept is simple. It’s like giving ChatGPT a personal notebook to review at the start of each session. This summary file should noticeably improve consistency. The AI will be less likely to contradict earlier conversations or ask for basic information again. It brings us closer to the kind of seamless, ongoing interaction that current memory systems are still trying to deliver.
Toward AI with True Long-Term Memory
Creating a memory profile summary is an intermediate fix, a bridge toward more sophisticated long-term memory in AI. In the future, I believe large language models will integrate much more robust memory systems. We might see architectures that can store and retrieve knowledge over indefinite time spans, or specialized memory components that work alongside the main model to recall facts about each user (with privacy controls, of course). The endgame is an AI that you don’t have to start from scratch with every time – one that can truly learn and evolve with you through every interaction.
To reach the level of meaningful interaction many users expect, AI needs memory that extends beyond a single session. No matter how advanced the model is, if it forgets everything you've said, the experience will always feel limited. Consistent chat profiles and long-term context are essential for depth and personalization. My project’s first step is the memory profile file. The next step will be expanding it into a broader system that can manage more context over time.
Eventually, I see this evolving into a full personal knowledge base that the AI can reference, not just summaries of chats but also documents, preferences, and relevant history, all under the user’s control. That kind of memory system is a larger challenge, but it's where this is headed.
Persistent memory is one of the key challenges in making AI systems more capable and reliable over time. This is the focus of the project I am working on. It involves building a structured, user-controlled memory layer that improves continuity across sessions. It is a step toward more useful and context-aware AI, and part of a broader effort to move these models closer to long-term, consistent interaction.