How do you keep AI up to speed on your project?
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I keep running into this while building with AI: after a few sessions, I end up re-explaining the project, the decisions we made, and what changed.
How do you handle that? Do you keep a project file updated, rely on chat history, or have another system that works?
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A file, and one boring rule that makes it work: never shorten it. Mine is called HANDOFF.md. Every session ends by writing what got done, what broke and the single next action at the top. The next session starts by reading the top. It's over two thousand lines now. Every time I let the model tidy it into a summary it lost the one detail I needed a week later, so now it only gets added to. Chat history I treat as gone.
@neelagiri_chettiyar 2000 lines and never shortened, ok thats the opposite of what i do. i keep trimming mine, maybe thats why it forgets stuff
@kumarr_rittik Probably. The trick is it never reads the whole thing. The top twenty lines are the current state and the next action that's all it reads at the start. The rest is there to search when a question comes up like why did we drop X and the answer is always some small detail a summary would have cut. Keep the top short and the bottom long.
An agent seemed to understand the codebase until it gave a clean fix with a green test. Reverting the code showed the test still passed because it had written a check that couldn't fail. Never trust its context until a failing test turns green.
@konstantin_tikhaev lol a test that cant fail. do you make it show the failing test first now?
@kumarr_rittik Make it write only the failing test first and run it. If it cannot produce a clean failure on the current code, it does not touch the fix.
Two layers. The durable one lives in the repo, not the chat: each project has a README written as its memory (what exists, what was tried and dropped; old conclusions get marked superseded, never deleted), and the roadmap has a section for things deliberately deferred, each with a reason. The second layer is my agent's long-term memory, which pulls relevant facts into each new session. One gotcha I just hit: it's scoped per working folder. A long session I started in /tmp kept everything it learned there, so none of it would show up when I open the agent inside the actual repo. Worth checking where yours stores things.
@wwenioaguiar wait its per working folder? didnt know that. going to check where mine saves
What would you include in a minimal project context files so the AI stays informed without wasting tokens on unnecessary details?
@ryangumm I would keep the current goal, constraints, run and test commands, and the next unfinished step
Link to longer decisions instead of copying them. Add the commit where the context was last checked so the next session can compare it with what changed
@ryangumm @ryangumm i'd also add what you already tried and dropped. otherwise it just suggests the same thing again
One big project file stops scaling fast. What worked for me: short ADRs for decisions, a typed, task-scoped memory the agent writes to as it goes, and checkpoints it resumes from instead of re-reading the whole chat. I open-sourced my setup as maggy if you want to steal ideas.
@ali_shaheen will check maggy. checkpoints is the part i dont have, mine re-reads everything every time
thanks all, didnt expect this many real answers. mostly hearing a file in the repo + never trim it + write down what you dropped.
i asked because i work on Pryveo, we're trying to fix the same thing from the other side (it keeps context from places you approve so you dont paste it every chat). still early. the handoff file ideas here are honestly useful for us too
Fair! The project file helps, but I need to see what changed since the last session or agents keep reviving dead ideas. I usually leave the rejected approach beside the decision.