Also important to note that human-written context did help according to them, if only a little bit.
Effectively what they're saying is that inputting an LLM generated summary of the codebase didn't help the agent. Which isn't that surprising.
Also important to note that human-written context did help according to them, if only a little bit.
Effectively what they're saying is that inputting an LLM generated summary of the codebase didn't help the agent. Which isn't that surprising.
I went through a couple of iterations of the CLAUDE.md file, first describing the problem domain and library intent (that helped target search better as it had keywords to go by; note a domain-trained human would know these in advance from the three words that comprise the library folder name) and finally adding a concise per-function doc of all the most frequently used bits. I find I can launch CC on a simple task now, without it spending minutes reading the codebase before getting started.
The article is interesting but I think it deviates from a common developer experience as many don't work on Python libraries, which likely heavily follow patterns that the model itself already contains.
> Their definition of context excludes prescriptive specs/requirements files.
Can you explain a bit what you mean here? If the context file specifies a desired behavior, we do check whether the LLM follows it, and this seems generally to work (Section 4.3).