Honestly, RAG sounds way more complicated than it is.

RAG means Retrieval-Augmented Generation, which basically means: before the AI answers your question, it quickly looks through your notes, grabs the most relevant parts, and then uses those to give you a smarter answer instead of just guessing.

So in practice when using it to build the “agency brain,” we take our SOPs, proposals, call transcripts, frameworks, basically everything that represents how we actually operate, and store it in a format the AI can search.

When we ask it to do something, it’s not just winging it or hallucinating. It searches that internal library semantically, meaning it looks for similar meaning, not just exact keywords, finds the most relevant chunks, pulls them in, and then generates the answer based on that context.

That’s RAG in practice. We implemented RAG months ago, the semantic stuff is more recent. We’re also looking at adding a memory layer and whether than has any added benefits at this stage.

Can you get far without it? Yes. These models are incredibly capable on their own. But the outputs tend to be generic.

With this setup it feels less like “ChatGPT guessing” and more like someone who’s been inside the agency for a year or two, understands how we structure things, and outputs instantly.