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Comment by lillecarl | Hacker News Reader

Isn't this just repackaged RAG pretty much?

Depends which definition of RAG you're talking about.

RAG was originally about adding extra information to the context so that an LLM could answer questions that needed that extra context.

On that basis I guess you could call skills a form of RAG, but honestly at that point the entire field of "context engineering" can be classified as RAG too.

Maybe RAG as a term is obsolete now, since it really just describes how we use LLMs in 2025.

I’d rather say you can use skills to do RAG by supplying the right tools in the skill (“here’s how you query our database”).

Calling the skill system itself RAG is a bit of a stretch IMO, unless you end up with so many skills that their summaries can’t fit in the context and you have to search through them instead. ;)

All skills are RAG, a subset of skills can add more RAG.

Seems like that’s it? You give it a knowledge base of “skills” aka markdown files with contexts in them and Claude figures out when to pull them into context.

I think RAG is out of favor because models have a much larger context these days, so the loss of information density from vectorization isn't worth it, and doesn't fetch the information surrounding what's retrieved.

That's true if you use RAG to mean "extra context found via vector search".

I think vector search has shown to be a whole lot more expensive than regular FTS or even grep, so these days a search tool for the model which uses FTS or grep/rg or vectors or a combination of those is the way to go.

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