Your first week of AI usage should be crawling your codebase and generating context.md docs that can then be fed back into future prompts so that AI understands your project space, packages, apis, and code philosophy.

I guarantee your internal tools are not revolutionary, they are just unrepresented in the ML model out of the box

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orra 3 days ago | parent | next [-]

That sounds incredibly boring.

Is it effective? If so I'm sure we'll see models to generate those context.md files.

	▲	cpursley 3 days ago \| parent [-]
		Yes. And way less boring than manually reading a section of a codebase to understand what is going on after being away from it for 8 months. Claude's docs and git commit writing skills are worth it for that alone.

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blitztime 3 days ago | parent | prev | next [-]

How do you keep the context.md updated as the code changes?

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shmoogy 3 days ago | parent [-]

I tell Claude to update it generally but you can probably use a hook

	▲	tombot 3 days ago \| parent [-]
		This, while it has context of the current problem, just ask Claude to re-read it's own documentation and think of things to add that will help it in the future

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nicce 3 days ago | parent | prev [-]

Even then, are you even allowed to use AI in such codebase. Is some part of the code "bought", e.g. commercial compiler generated with specific license? Is pinky promise from LLM provider enough?

	▲	GenerocUsername 3 days ago \| parent [-]
		Are the resources to understand the code on a computer? Whether it's code, swagger, or a collection of sticky notes, your job is now to supply context to the AI. I am 100% convinced people who are not getting value from AI would have trouble explaining how to tie shoes to a toddler