RAG done right: grounding AI in your own data
Retrieval-augmented generation is how you get an AI that answers from your reality, not the internet.
An off-the-shelf model knows a lot about the world and nothing about your business. Retrieval-augmented generation (RAG) closes that gap: before the model answers, you fetch the relevant facts from your own documents and hand them over as context.
The pieces that matter
- Ingest and chunk your sources so each piece is retrievable on its own
- Embed and index them for fast semantic search
- Retrieve the right passages for each question — quality here decides everything
- Ground the answer in those passages, and cite them so it can be checked
Where it goes wrong
Most bad RAG is a retrieval problem, not a model problem. Chunks too big drown the signal; too small lose context. If the right passage never gets retrieved, no model can save the answer. We spend our time on retrieval quality and evaluation — that is where reliability lives.