[alextillman]

This doesn't resonate:

Consider this prompt:

Make sure that, whenever a job runs, we can tell where it started from, who triggered it, what settings were passed in, and what changed later. In the future, if something breaks, we want to be able to trace it back and understand what happened.

That is fine.

But an expert can say:

Jobs should persist provenance metadata."

That only works if the model is trained, specifically the way you want, to understand that second sentence. If not, any model could work with the first sentence, but not with the second.

You've crated a need for expert training at the model level (which is insanely expensive to create and maintain) rather than accessible natural language discussions that work on any model and understood by anyone. Denser isn't "better" because the words have more power.

[kstenerud]

The big models already understand this. I use nomenclature and jargon such as this all the time when using Claude Code on a codebase.

[alextillman]

Right, but you still don't know that the big model has exactly the same "jargon" handling as you do. Plus it means that you can't use smaller models not trained on that jargon. That seems like a really limiting black box to place at the heart of a system.

[justanotheratom]

with skills, model can easily learn the jargon.