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People were doing this somewhat effectively with garbage Markov chains and it was 'ok'. There is research going on right now doing precisely what I described. I know because I wrote a demo for the researcher whose team wanted to do this, and we're not even using fine tuned LLMs. You can overcome much of the issues around 'hallucinations' by just repeating the same thing several times to get to a probability. There are teams funded in the hundreds of millions to build the engineering around these things. Wrap calls in enough engineering and get the bumper rails into place and the current generation of LLM's are completely capable of what I described.
This current generation of AI revolution is just getting started. We're in the 'deep blue' phase where people are shocked that an AI can even do the thing as good or better than humans. We'll be at alpha-go in a few years, and we simply won't recognize the world we live in. In a decade, it will be the AI as the authority and people will be questioning allowing humans to do certain things.
Read a little further. I might disagree with you about the overall capability/potential of AI, but I agree this is a great task to highlight its strengths.
Sure. and yes I think we largely agree, but on the differences, I seen that they can effectively be overcome by making the same call repeatedly and looking at the distribution of results. Its probably not as good as just having a better underlying model, but even then the same approach might be necessary.