You'd want to have the alteration reference existing guides to the current implementation.

I haven't jumped in headfirst to the "AI revolution", but I have been systematically evaluating the tooling against various use cases.

The approach that tends to have the best result for me combines a collection of `RFI` (request for implementation) markdown documents to describe the work to be done, as well as "guide" documents.

The guide documents need to keep getting updated as the code changes. I do this manually but probably the more enthusiastic AI workflow users would make this an automated part of their AI workflow.

It's important to keep the guides brief. If they get too long they eat context for no good reason. When LLMs write for humans, they tend to be very descriptive. When generating the guide documents, I always add an instruction to tell the LLM to "be succinct and terse", followed by "don't be verbose". This makes the guides into valuable high-density context documents.

The RFIs are then used in a process. For complex problems, I first get the LLM to generate a design doc, then an implementation plan from that design doc, then finally I ask it to implement it while referencing the RFI, design doc, impl doc, and relevant guide docs as context.

If you're altering the spec, you wouldn't ask it to regen from scratch, but use the guide documents to compute the changes needed to implement the alteration.

I'm using claude code primarily.

Hm, maybe it's me who misunderstands the workflow. In that case I agree with you.

That said, I think the non-determinism when rerunning a coding task is actually pretty useful when you're trying to brainstorm solutions. I quite often rerun the same prompt multiple times (with slight modifications or using different models) and write down the implementation details that I like before writing the final prompt. When I'm not happy with the throwaway solutions at all I reconsider the overall specification.

However, the same non-determinism has also made me "lose" a solution that I threw out and where the real prompt actually performed worse. So nowadays I try to make it a habit to stash the throwaway solutions just in case. There's probably something in Cursor where you can dig out things you backtracked on but I'm not a power user.

You would need to rerun the LLM, but you wouldn't necessarily need to rebuild the codebase from scratch.

You can provide the existing spec, the new spec, and the existing codebase all as context, then have the LLM modify the codebase according to the updates to the spec.