Capturing Context

When Your Best AI Insights Disappear

There's a moment in every extended AI coding session where you realize you've built something valuable—not just the code you set out to write, but the collective understanding that emerged from the back-and-forth. The debugging insights, the edge cases discovered, the architectural decisions explored. Then your context window hits its limit, you start a fresh conversation, and most of that accumulated knowledge vanishes.

I've been thinking about this a lot lately, especially after watching Claude work through a particularly gnarly debugging session. The solution we eventually reached wasn't the only valuable output—the journey itself contained insights I didn't want to lose.

The Context Window Reality

While the industry races toward longer context windows—Google's Gemini 1.5 Pro now handles up to 2 million tokens, and researchers have tested up to 10 million—most of us still hit practical limits regularly. Tools like Claude Code and Cursor have gotten sophisticated about managing context through compression and intelligent scoping, but there's still a fundamental constraint.

Context windows max out, conversations reset, and that accumulated understanding gets wiped clean. What's lost isn't just the conversation history—it's the contextual intelligence that builds up along the way.

A Debugging Session That Taught Me Something

I noticed this clearly during a recent code review where I was working with Claude to track down a persistent bug. The primary task was straightforward enough, but as we worked through it, Claude kept encountering interesting patterns—not blocking issues, just things worth noting.

Claude stayed focused on the main task rather than getting sidetracked, which was good. But I found myself mentally bookmarking these observations, knowing they'd be valuable later. A potential race condition in the async handler. An inconsistent error handling pattern across similar functions. A dependency version that might be causing subtle issues downstream.

When we finally resolved the main bug, I had Claude summarize these secondary observations and create GitHub tickets for each one using the command line tools. The key was being specific about which patterns I was referring to—"that async handler issue you mentioned earlier" or "the error handling inconsistency in the auth flow."

Here's an example of exactly this kind of ticket, created by Claude at the end of our debugging session: https://github.com/bobmatnyc/ai-code-review/issues/38

What I like about this ticket is how it captures not just the technical issue, but the context of how we discovered it—the kind of nuanced understanding that only emerges through actual debugging work but would otherwise be lost when the conversation resets.

Beyond Bug Reports: Building Knowledge Assets

This technique extends well beyond coding tasks. Any time you're working through a complex problem with an AI assistant, valuable knowledge accumulates that's worth preserving.

When I anticipate hitting context limits, I ask Claude to create artifacts that capture key learnings. Sometimes it's a technical how-to document. Other times it's a fact sheet summarizing what we discovered about a particular tool or approach. Occasionally it's just a memo outlining the decision-making process we went through.

The format matters less than the practice of actively preserving insights before they're lost. I either save these to the project I'm working on or export them as markdown files for future reference.

Three things I'll call out here:

Be specific about what to preserve. Don't ask for generic summaries. Reference particular patterns, edge cases, or insights you want captured: "Document that caching issue we discovered in the payment flow" rather than "summarize our session."

Choose the right format for future use. GitHub tickets for actionable items, documentation for complex processes, decision logs for architectural choices. Think about who will need this information and when.

Capture decisions and reasoning, not just facts. The "why" behind choices often proves more valuable than the "what" when similar situations arise later.

The Current Landscape

Some AI assistants handle context management better than others. Claude Code maintains context across longer sessions, GitHub Copilot Chat keeps conversation history within VS Code, and Cursor maintains some context between different chat instances.

The most interesting developments are happening at two extremes:

Automated memory systems like Mem0 take the sophisticated approach—automatically extracting and consolidating salient information from conversations using AI. Their system achieves 26% higher accuracy than OpenAI's memory while reducing token costs by 90%, essentially providing the kind of persistent, intelligent memory I mentioned in the conclusion.

Manual context management like Roo Code's Memory Bank takes the practical approach—structured markdown files that track project context, decisions, and progress. As one developer noted: "Once I integrated a solution for this, it boosted Roo Code's effectiveness dramatically." It's essentially the artifact approach I've been using, but systematized.

But even with these improvements, there's still what I think of as a "session firewall"—limited connection between one conversation and the next. The knowledge accumulated in Tuesday's debugging session doesn't automatically inform Thursday's feature development, even when they're related.

Making This a Habit

I've started building this into my regular AI workflow:

During the session: Pay attention to insights that emerge organically, not just the primary task progress.

Before context limits: Ask for summaries or artifacts that capture valuable observations.

Be specific: Reference particular patterns or issues you want preserved, rather than asking for generic summaries.

Choose the right format: Sometimes it's GitHub tickets, sometimes documentation, sometimes just organized notes.

The goal isn't to capture everything—that would be overwhelming and counterproductive. It's about recognizing when genuine insights emerge and making sure they don't disappear when the conversation resets.

Looking Forward

The solutions are starting to emerge at both ends of the spectrum. Mem0's automated memory extraction shows what sophisticated memory systems can achieve—automatically identifying and consolidating important facts from conversations with 91% lower latency than full-context approaches. Meanwhile, Roo Code's Memory Bank demonstrates how structured project documentation can maintain context through simple markdown files.

But until these approaches become mainstream, actively capturing context remains one of those small techniques that can significantly improve your AI workflow productivity. You're not just solving today's problem—you're building a knowledge base that makes tomorrow's problems easier to tackle.

The conversations we have with AI assistants are becoming increasingly valuable. It's worth making sure that value doesn't disappear when the conversation ends.