Comment by iainmerrick
10 hours ago
To clarify, the bit where I think the bitter lesson applies is trying to standardize the directory names, the permitted headings and paragraph lengths, etc. It's pointless bikeshedding.
Making your docs nice and modular, and having a high-level overview that tells you where to find more detailed info on specific topics, is definitely a good idea. We already know that when we're writing docs for human readers. The LLMs are already trained on a big corpus written by and for humans. There's no compelling reason why we need to do anything radically different to help them out. To the contrary, it's better not to do anything radically different, so that new LLM-assisted code and docs can be accessible to humans too.
Well-written docs already play nicely with LLM context.
I've never felt that generic, share-able skills were much of a useful thing. The model already knows the generic things!
On the other hand, the specific details of how to do things in YOUR software, in YOUR environment (especially if it's quirky) - that does beat asking the model to work it out from first principles each time.
I still prefer to rely on convention where possible - i.e, what's even better than writing a skill for say, "how to manage users in our ABC application, first call API a..." is to follow some widely established convention that means the obvious first thing the model would naturally try "just works".
Is your view that this doesn’t work based on conjecture or direct experience? It’s my understanding Anthropic and OpenAI have optimized their products to use skills more efficiently and it seems obviously true when I add skills to my repo (even when the info I put there is already in existing documentation).