Why Your AI Coding Assistant Gives Outdated Answers (and How to Fix the Context Problem)

A full-stack developer who supports the product Pryveo describes how AI coding assistants give outdated answers because they only know their training data and whatever context is placed in front of them, not the current state of a project. The developer recommends maintaining a plain Markdown CONTEXT.md file at the repository root — capturing the current goal, dated decisions, rejected ideas, and open questions — and feeding it to the assistant at the start of each session, while noting the approach breaks down when decisions are never written down or when teams need shared, permissioned memory.

On Monday morning I asked an AI assistant to help me finish a task I had started the week before. The answer came back in seconds, it was well written, and it was wrong. The approach it recommended was the one my team had dropped on Thursday, in a message thread the assistant had never seen. The model did its job. It answered from the information it had, and that information was a week old. I am a full stack developer, and I also support a product called Pryveo that works on this exact problem, so I have spent a lot of time looking at how developers deal with it. This post covers why it happens, how to spot it, a fix you can apply today with a plain Markdown file, and where that fix stops working. A model only knows two things: what it learned in training and what you put in front of it. Your project lives in neither place by default. The current state of real work is spread across tools: Every new chat starts without any of this. You either paste the background in again or the assistant fills the gap with a reasonable guess. A reasonable guess based on last week's facts reads exactly like a correct answer, which is what makes this failure hard to notice. If two or more of these feel familiar, the problem is in your context and a different model will not solve it. The developers I have spoken to who handle this well all do some version of the same thing. One points every new chat at a folder of notes. Another finishes and tests each task before stopping, because he never knows when he will be back. He told me his last commit was 28 days old when he returned to a side project, and that habit is what let him continue. The simplest version is a single file at the root of the repository: CONTEXT.md Current goal Ship the invoice export by Friday. CSV only, PDF is out of scope. Decisions newest first - 2026-10-02: Dropped the queue-based approach. Export runs inline because files are small. Decided in the team thread after load testing. - 2026-09-29: Dates are stored in UTC and formatted in the client. Rejected ideas - Streaming the export: adds complexity we do not need at this size. Open questions - Does the finance team need a totals row? Three rules make this file useful: Then start each session by giving the assistant this file first. The quality of the answers changes immediately, because the assistant is now working from this week. I use this approach and I recommend it. It also has clear limits. That last point matters more as teams adopt agents. Memory for AI is partly a storage problem and largely a permissions problem: someone has to decide what gets remembered and what gets shared. This is the gap the Pryveo team is working on. The idea is to keep approved context ready, so you and your AI can pick up where you left off. A few parts of the design are relevant to the limits above: One honest limit applies to any tool in this space, including this one: if a decision was never written down anywhere, nothing can retrieve it. The habit of recording the reason behind a decision still matters. You can see how it works at pryveo.com https://www.pryveo.com/ . I am collecting the ways developers keep their AI tools current, and the answers so far are more varied than I expected. How do you bring an assistant up to date at the start of a session: a notes file, a rules file, pasted threads, or something else?