Context is Key: Why AI Memory Matters More Than You Think

By: Travis Fleisher

One of the most overlooked challenges for new AI adopters is understanding how large language models handle memory and context. While AI tools are incredibly powerful in the moment, their usefulness often breaks down when you try to maintain continuity across sessions. Whether you're analyzing documents, building a product, or working on a long-term strategy, the ability to reference prior work or maintain an ongoing thread with your AI assistant becomes critical. Unfortunately, most models aren't great at remembering where they left off.

In AI terms, context refers to how much recent information the model can process at once, measured in tokens (a token is roughly a word or two). Memory, on the other hand, refers to how much the model retains over time—across sessions, documents, or projects. Today’s tools generally excel at short-term context but struggle with persistent memory. This limitation creates a real problem for people using AI for more than casual queries. You might upload a file, have a productive session, and then return the next day only to find your AI assistant has no recollection of the work you just did. That lack of continuity turns what should be a seamless collaboration into a repetitive process of re-explaining and re-contextualizing.

Understanding how different tools manage context and memory is essential to choosing the right one for your workflow. Here’s a snapshot of how three major platforms compare:

ChatGPT (GPT-4)
Memory style: Chat memory and custom instructions, with “Projects” in early rollout
Context window: ~32K tokens

Claude 3
Memory style: Project-based memory with multi-file document support
Context window: ~200K tokens

Microsoft Copilot
Memory style: Grounded in Microsoft Graph, including email, documents, and calendar
Context window: ~16K–32K tokens

For reference, tokens are the units of text that language models process. A token typically equals a word or part of a word—“ChatGPT,” for example, might be split into multiple tokens. The larger the token limit, the more information a model can consider at once, which directly affects how well it understands and responds within a single session.

Claude currently leads in both memory design and context length. Its “Projects” feature lets you create a discrete workspace where the model can interact with multiple documents, recall earlier interactions, and develop a coherent understanding of your task. ChatGPT is in the process of rolling out a similar feature and already allows for a degree of personalization through custom instructions. Microsoft Copilot takes a different approach, focusing on contextual grounding in enterprise data, but lacks flexibility for general-purpose workflows.

Until persistent memory becomes a universal feature, there are a few ways to work around these limitations. One is to use pinned or ongoing conversations within ChatGPT or Claude to maintain continuity. Another is to centralize your context in a reference document that you can reuse across sessions. You can also take advantage of tools like Claude Projects to build AI environments that function more like a digital workspace than a single-use assistant.

At TwinBrain, we’ve run into these challenges firsthand. Whether we’re testing product features, building AI workflows, or managing research tasks, the absence of persistent memory has been a limiting, and frustrating, factor. But by understanding each tool’s strengths and constraints, we’ve been able to build systems that work with the tools—not against them.

If you’re just beginning your AI adoption journey, don’t overlook the importance of memory and context. It may seem like a technical detail, but it fundamentally changes what these tools can do for you.

Companies Tackling the Memory Problem

Several companies are making meaningful strides in solving context and memory limitations in LLMs. Here are a few worth watching:

Anthropic (Claude Projects)
Anthropic’s Claude 3 is currently the most robust solution for persistent memory in long-form and multi-document work. Projects allow users to create self-contained environments that remember context, store files, and evolve over time. Ideal for researchers, analysts, and product builders.

OpenAI (ChatGPT Projects + Custom GPTs)
OpenAI has introduced early access to “Projects” in ChatGPT, aiming to bring long-term memory to the platform. Custom GPTs also allow for embedding context and task-specific behavior directly into a persistent assistant, although these still rely on users to provide context at the outset.

Rewind.ai
Rewind records everything you’ve seen, said, or heard on your device and makes it searchable. While not a chatbot, it gives users personal memory recall and can integrate with LLMs to provide personalized context for prompts and tasks.

Mem.ai
Mem is a productivity tool designed around memory-first workflows. It integrates note-taking, task tracking, and contextual recall into a single space, with AI support layered on top. Useful for users who want their past inputs and documents to inform their future interactions.

Dust.tt
Dust allows teams to build AI agents powered by custom data and documentation. Unlike traditional chat tools, Dust emphasizes context persistence and retrieval-augmented generation (RAG), making it a great option for internal company use cases.

Useful Tools to Explore

• Notion AI: Offers lightweight memory within workspaces, useful for recurring workflows

• ChatPDF / Humata.ai: Tools for uploading and chatting with documents, though they reset context between sessions

• LangChain / LlamaIndex: For developers building custom context-aware agents using vector databases and retrieval strategies

As AI continues to evolve, persistent memory and contextual awareness will define the next wave of useful, reliable tools. For now, the key is choosing the right setup for your specific needs and learning to work with the memory constraints—not against them.

Travis