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Is it data or the mechanics?

One-shot performance of LLMs is a good measure of many of its capacities, but it is only sufficient for tasks that can be objectively and automatically verified. I believe that in most real-world cases this is not the case: people have vague, unquantifiable, taste-based, complex goals, and it's the way one works closely with AI over many prompts that makes the difference between slop and actual work. But few people seem to talk about that today. This blog post (and the browsable prompt trascripts it links to) is an attempt at changing that.

I'm sharing all my prompts for a little toy web-app project. It's the opposite of one-shot! I also (over?)analyzed and charted my data. I'd love to hear everyone's thoughts, not so much on the exact execution (debatable) but on my proposal to talk more about HITL AI workflows.

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My partner and I have been regularly watching movies for several years now. It became more than a hobby over time. However, we usually get frustrated when we cannot decide what to watch. Movie ratings are good, but they're not enough to tell us if that fits our taste and mood. Movie forums are great, but it's also very dense, and finding something could turn into a weekend project. What we want, for instance, was somebody saying “you should definitely watch this, it has one of the best child performances I've ever seen," which tells us why we should watch that movie.

We built an app around this idea, where there's a deck of cards, and each card is a movie someone recommended, with why you should watch it. It's a shuffled deck with no ranking and no recommendation algorithm. Free, no ads, no subs, and no signup. There are about 700 cards right now, collected from about 400 people. Go ahead and try.