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100%. Useful cases include

* figuring out how to X in an API - eg "write method dl_file(url, file) to download file from url using requests in a streaming manner"

* Brainstorming which libraries / tools / approaches exist to do a given task. Google can miss some. AI is a nice complement for Google.




I don’t even trust the API based exercises anymore unless it’s a stable and well documented API. Too many times I’ve been bitten by an AI mixing and matching method signatures from different versions, using outdated approaches, mixing in apis from similar libraries, or just completely hallucinating a method. Even if I load the entire library docs into the context, I haven’t found one that’s completely reliable.


It just has to be popular and common boilerplate like the example I gave.

It's hard with less popular APIs. It will almost always get something wrong. In such cases, I read docs, search sourcegraph / GitHub, and finally check the source code.




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