There's a pattern anyone who works with Claude Code, Cursor, or similar tools for more than a couple of weeks recognizes, even if they've never quite put a name to it: the first sessions on a new project are almost magical. The AI understands quickly, proposes sensible solutions, the code works nearly every time on the first try. Then, after the tenth or twentieth session, something changes. Answers start contradicting choices made two weeks earlier. Two different ways of doing the same thing show up in the same file. A bug you thought was fixed comes back, because the AI touched a function again without knowing it had already been corrected for a specific reason.
The instinctive reaction is to think the model is "getting worse," or that you need a more powerful one. In most cases, that's not the problem. The problem is simpler and less talked about: the context the AI has of your project doesn't grow along with the project. It stays flat, or worse, it degrades.
What actually happens, session after session
A language model has no memory of its own between sessions (we cover this in more depth here). Every time you open a new conversation, it starts from whatever you have it read at that moment: the project files, maybe an instructions file like CLAUDE.md, and whatever you write in the prompt. Everything else, every decision made together in previous sessions, is lost unless it ended up somewhere persistent.
In the early sessions this goes unnoticed, because the project is small: reading the existing code is enough to reconstruct the necessary context. But a project that grows also accumulates decisions that aren't written in the code. Why you chose that library and not another. Why that function looks oddly written but is actually a workaround for a specific bug in a dependency. Which pattern the rest of the codebase follows, even if that particular file hasn't caught up yet. This information lives in your head, not in the code, and an AI that only reads files never gets there.
The result is what you could call context debt: the gap between what the AI should know to work well on the project and what it can actually reconstruct by reading only the files. The bigger the project grows, the wider that gap gets, and the more the AI's suggestions start drifting from how the project is actually meant to work.
It's not (only) a "better prompts" problem
A common answer is: write more detailed prompts, explain the context better every time. That's a band-aid, not a cure. It works for one session, but the cost of reconstructing context out loud falls on you every single time, and it grows with the project's complexity instead of shrinking. Another common remedy, padding the project's instructions file with more and more detail, works up to a point and then starts suffering from the same problem as any context that's too long: key information buried among a lot of other text carries less weight than it should, simply because the model struggles to distribute attention evenly over a very long text.
The point isn't "explain better." It's to stop relying only on re-explaining, and instead give the AI a place where the relevant context is already organized and retrievable on demand, instead of dumped in full every time.
Three symptoms to recognize before they become a serious problem
Duplicated code with slightly different logic. Two functions that do almost the same thing, written at different times, because the AI didn't know the first one already existed.
Architectural decisions silently reversed. A choice made for a specific reason (a data format, an authentication scheme, a folder structure) gets quietly changed in a later session, because that reason wasn't written anywhere the AI could read.
Fixed bugs that come back. The most frustrating case: a problem fixed with a targeted patch, that reappears because a future session touches the same code without knowing that detail had already been handled.
None of these three is a sign that "the model got worse." They're all signs that the project has accumulated more implicit context than the AI can retrieve from the files alone.
What actually helps
The practical difference we've seen work, and what the VibeCoded Orchestrator is built on, is separating two things that often get conflated: the code, which describes what the project does, and the decisions, which describe why it's built a certain way. The former lives well in source files. The latter needs its own searchable place, one that survives across sessions: a knowledge graph with nodes linked to each other, not another text file that grows forever.
It's not a change you see on day one. You see it at month three, when you reopen the project after a break, or when a colleague touches it for the first time, and the question "why is it built this way?" already has an answer written down somewhere, instead of having to reconstruct it by reading old commits or asking around.
If you want the practical guide to wiring this kind of memory into your project, step by step, it's here.