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Its a bit of a game changer if you already understand things so can point it to the right tasks and also clock that it's screwed up just from glancing at the co

• intended 3y ago
  I should probably write a post about this, but comments are easier. 0) LLMs introduce the challenge of automated semantic verification. 1) LLM work should be broken up by usecase. These use cases lie on a semantic complexity continuum. 2) Tasks like classification are simple to verify (precision recall etc.). Tasks that require semantic complexity like summarizaiton are on the other end. 3) Anything on the high semantic complexity end of the scale needs expert human review. 4) Chained or complex calls greatly complicate verification Which is why you are getting use out of it. There is a human (you) in the loop, and the calls are not complex. As long as a human is reviewing the output of the LLM every time, its great. This is the vast majority of "generative" output. Its when you have things like agents, or chained calls that things go awry. ITs why proof of concepts are easy, but production is hard. For the record, many people have called this out, including people at Open AI, and AI ops was the largest sub group in YC's fall batch.