What is RAG, and do you actually need it

RAG (retrieval-augmented generation) is a technique for handling more information than fits comfortably in one context window: instead of giving Claude everything up front, a system first searches a large pile of documents for the relevant pieces, then hands just those to Claude to answer from.

You’ll see this term thrown around a lot in AI-tooling marketing, often to sound sophisticated. For most founders, here’s the honest version: if your memory files and the documents you’re working with fit in a normal conversation — your GOALS.md, a spreadsheet, a folder of interview transcripts — you don’t need RAG. You just paste or point Claude at the real thing directly. It becomes relevant once you have a genuinely large, ever-growing body of documents (a huge support-ticket archive, years of meeting notes) where finding the right slice matters more than having all of it in view at once.

If you ever evaluate a tool that advertises “RAG-powered,” the real question is the same one that matters everywhere else: does it retrieve the actually relevant piece, or does it retrieve something plausible-sounding that isn’t quite right? Ask for examples before you trust it with anything important.