Comment by wat10000
20 days ago
It’s not very good in context, for one thing. Context isn’t that big, and RAG is clumsy. Working with an LLM agent is like working with someone who can’t form new long term memories. You have to get them up to speed from scratch every time. You can accelerate this by putting important stuff into the context, but that slows things down and can’t handle very much stuff.
The article does demonstrate how bad it is in context.
Context has a lot of big advantages over training though, too, it's not one-sided. Upfront cost and time are the big obvious ones, but context also works better than training on small amounts of data, and it's easier to delete or modify.
Like even for a big product like Claude Code from someone that controls the model, although I'm sure they do a lot of training to make the product better, they're not gonna just rely entirely on training and go with a nearly blank system prompt.