Also the thing with context is that you want to keep it focused on the task at hand.

For example there's evidence that typical use of AGENTS.md actually doesn't improve outcomes but just slows the LLMs down and confuses them.

In my personal testing and exploration I found that small (local) LLMs perform drastically better, both in accuracy and speed, with heavily pruned and focused context.

Just because you can fill in more context, doesn't mean that you should.

The worry I have is that common usage will lead to LLMs being trained and fined tuned in order to accommodate ways of using them that doesn't make a lot of sense (stuffing context, wasting tokens etc.), just because that's how most people use them.

▲

ravikirany22 3 hours ago | parent [-]

This matches what we've been seeing empirically. The issue isn't just quantity of context — it's staleness. AGENTS.md and CLAUDE.md that reference renamed functions, deleted interfaces, or outdated patterns actively mislead the model with confident but wrong information.We've been auditing TypeScript repos and finding 10-84% of symbol references in AI config files are stale. A model reading a CLAUDE.md that says "use UserService.createUser()" when that function was renamed three weeks ago isn't just getting irrelevant context — it's getting a confident lie.The quality problem is probably as significant as the quantity problem, maybe more so.

	▲	dgb23 an hour ago \| parent [-]
		Interesting. It seems to me that the right approach is to have a structured way to navigate a codebase and useful, validated docs (with examples that need to pass tests) rather than ad-hoc markdown prompts laying around and are always read. We already have solutions for this like doc comments/strings, meta data etc. The codebase itself needs to be well-maintained.