Not GP, but your appendix about LLM usage matches exactly how I use it too: mainly for rubber ducking and research. The codegen it's useful for (that I've found) is generating unit tests. Code coverage tools and a quick skim are more than sufficient for quality checks since unit tests are mostly boilerplate and you want to make sure that different branches are being covered.
I've had a large project recently which has biased my view on unit testing from LLMs. It includes a lot of parsing and other workflows that require character-specific placement in strings for a lot of tests. Due to how tokenization works, that is a gnarly use case for LLMs. I am trying not to form too many strong opinions about LLM-driven-TDD based on it. My forays into other domains show better results for unit tests, but the weight of my experience is in this particular low point lately.
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Not GP, but your appendix about LLM usage matches exactly how I use it too: mainly for rubber ducking and research. The codegen it's useful for (that I've found) is generating unit tests. Code coverage tools and a quick skim are more than sufficient for quality checks since unit tests are mostly boilerplate and you want to make sure that different branches are being covered.
I've had a large project recently which has biased my view on unit testing from LLMs. It includes a lot of parsing and other workflows that require character-specific placement in strings for a lot of tests. Due to how tokenization works, that is a gnarly use case for LLMs. I am trying not to form too many strong opinions about LLM-driven-TDD based on it. My forays into other domains show better results for unit tests, but the weight of my experience is in this particular low point lately.