One of the skills needed to effectively use AI for code is to know that telling AI "don't commit secrets" is not a reliable strategy.

Design your secrets to include a common prefix, then use deterministic scanning tools like git hooks to prevent then from being checked in.

Or have a git hook that knows which environment variables have secrets in and checks for those.

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jeroenhd 14 hours ago | parent [-]

That's such an incredibly basic concept, surely AIs have evolved to the point where you don't need to explicitly state those requirements anywhere?

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simonw 12 hours ago | parent | next [-]

They can still make mistakes.

For example, what if your code (that the LLM hasn't reviewed yet) has a dumb feature in where it dumps environment variables to log output, and the LLM runs "./server --log debug-issue-144.log" and commits that log file as part of a larger piece of work you ask it to perform.

If you don't want a bad thing to happen, adding a deterministic check that prevents the bad thing to happen is a better strategy than prompting models or hoping that they'll get "smarter" in the future.

	▲	eichin 3 hours ago \| parent [-]
		Part of why these things feel "not fit for purpose" is that they don't include the things Simon has spent three years learning? (I know someone else who's doing multi-LLM development where he uses job-specialty descriptions for each "team member" that lets them spend context on different aspects of the problem; it's a fascinating exercise to watch, but it feels even more like "if this is how the tools should be used, why don't they just work that way"?)

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thunky 12 hours ago | parent | prev [-]

Doesn't seem to work for humans all the time either.

Some of this negativity I think is due to unrealistic expectations of perfection.

Use the same guardrails you should be using already for human generated code and you should be fine.