One important thing I learned early:
By default, Replit will overwrite and summarize its own generated documentation over time.
That’s useful for keeping things concise, but it also means:
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important details can get compressed away
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subtle but critical facts can disappear
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the agent may lose context across sessions
So if you want stable, long-lived reference context, you need to create documentation that the agent does not rewrite without your intent.
The problem I kept running into
Each new agent session would try to reconstruct the system from code:
sometimes correctly
sometimes subtly wrong
Example from one app:
It would always assume LocalMusicX.com dev and prod shared the same database
They don’t (separate Neon DBs)
Another example (even trickier)
In my other app, http://WordsYouKnow.app, the system supports:
5 Romance languages
bidirectional learning (native → target AND target → native)
So the UI, lesson content, and prompts all depend on:
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the learner’s native language
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the target language they’re learning
Without context, the agent would repeatedly try to re-derive:
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which language each part of the UI should be in
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how lessons are localized
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which direction the translation flow should go
That’s a lot of hidden logic to “rediscover” from code.
The fix: give the agent a persistent source of truth
Instead of relying on prompts or auto-generated docs, I built a small set of stable reference files.
Now I can say:
“Please refer to the application localization documentation and implement…”
…and the agent stays aligned.
This has been an enormous productivity multiplier.
What I’m using for LocalMusicX.com
1. AGENT_NOTES.md (append-only, project root)
The most critical file.
Captures facts that are easy to get wrong and expensive to rediscover, like:
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dev vs prod DB separation
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venue calendars are the ONLY authoritative source for events
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venue → event city cascade behavior
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communication / coding preferences
Rule: append-only (never rewrite history)
2. docs/project-history.md
A full system snapshot:
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tech stack
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DB schema
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frontend routes
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backend services
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major development phases
Gives a new session a map of the system before touching code.
3. docs/testing-reference.md
Keeps testing consistent:
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test procedures
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data-testidconventions -
DB references
Helps the agent write reliable tests instead of guessing.
4. docs/future-development-goals.md
Prevents accidental conflicts with the roadmap.
Without this, the agent may:
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implement features in the wrong way
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or conflict with planned architecture
5. Session scratchpad (in conversation context)
For temporary but critical knowledge:
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in-flight bugs
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edge cases
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implementation quirks
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ordering rules / gotchas
Examples:
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route shadowing rules
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early-return ordering
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specific React warnings
Doesn’t belong in permanent docs—but essential during multi-session work.
The result
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far fewer incorrect assumptions
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less re-explaining context
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more consistent changes across sessions
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fewer regressions
My takeaway
AI agents don’t maintain a stable mental model of your system.
If you don’t provide one, they will keep rebuilding it
And sometimes rebuilding it wrong
And if your docs are being auto-summarized, that model can drift even faster.
Curious how others are handling this
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Are you maintaining an
AGENT_NOTES.mdor similar? -
How do you protect critical context from being overwritten?
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Do you rely on prompts or persistent docs?
Feels like this is becoming a core discipline for AI-assisted development.