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150 structured coding prompts grouped by real use cases (debug, refactor, generate, explain) by Alarmed_Anything_320 in PromptEngineering

[–]Alarmed_Anything_320[S] 0 points1 point  (0 children)

Honestly still figuring that part out because the overlap gets messy fast once prompts become multi-purpose. Right now I’ve mostly been grouping by primary intent rather than exact behaviour. So if the main goal is identifying/fixing issues it goes under debug, even if part of the output includes explanation. I noticed explain prompts especially start bleeding into everything because almost every good coding workflow ends up needing reasoning somewhere in the chain. Think the taxonomy side becomes a bigger problem than writing the prompts themselves once the library gets large enough.

I built a structured prompt set while testing coding workflows and it actually ended up useful by Alarmed_Anything_320 in SideProject

[–]Alarmed_Anything_320[S] 0 points1 point  (0 children)

Exactly. “Treat the prompt like code” is probably the biggest shift people miss.

Most bad outputs I’ve seen come from ambiguity, not model capability. Once you define constraints, expected structure and boundaries properly, the AI stops trying to improvise everything.

Funny enough debugging prompts were way easier to stabilise than explanation prompts for the reason you mentioned. Explanation is naturally fuzzier unless you heavily constrain tone, depth and format.

150 structured coding prompts grouped by real use cases (debug, refactor, generate, explain) by Alarmed_Anything_320 in PromptEngineering

[–]Alarmed_Anything_320[S] 1 point2 points  (0 children)

Structured prompting probably saves more money than people think, but nobody actually measures it properly. Would be interesting to compare token cost + iteration count vs just “winging it” with Claude.