Last year I did an experiment with ChatGPT and Advent of Code. I was thinking of repeating it and since last year I was criticized for choice of model and prompt I'm going to crowdsource them: which LLM should I use, which one is best at writing code? What prompt should I give it?
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So, I had a little more success than you last year, and you can see my transcript here. Part of the reason is that I didn't give it a minimal prompt. Try to give it full context for what it's doing - this is an LLM, not a Google search, and brevity hurts it. And don't "help" it by removing the story from the problem - after all, English comprehension is its strength. Tell it, up front, exactly how you're going to interact with it: it can "think step by step", it can try some experiments on its own, but you won't help it in any way. The only thing you'll do is run the code it gives you, or submit an answer to the site, telling it the (exact) error message that AoC generates.
To reiterate, give it all the information a human solving AoC would have. That's the fairest test.
My prediction is that o1 will do better (of course), maybe solving a few in the day 10-20 range. However, I think it'll still have problems with certain problems, and with debugging, especially when text output (or a diagram in the input) needs to be parsed character-by-character. This is a fundamental problem with LLMs: textual output that looks well-formatted and readable to us is fed into the LLM as a gobbledegook mixture of tokens, and it just has no clue how to process it (but, sadly, pretends that it can). This is related to how they have trouble with anagrams or spelling questions (e.g. how many Rs are in "strawberry"). I wonder if there's some way we could process text output so it tokenizes properly.
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