Large language models are powerful but don't know your company's documents. Retrieval-augmented generation (RAG) fixes that by searching your content first and giving the most relevant passages to the model before it answers.
How RAG works
- Ingest: documents are cleaned, split into chunks and indexed (vector + keyword search).
- Retrieve: for each question, the most relevant chunks are found and re-ranked.
- Generate: the model answers using only those chunks and cites its sources.
Where businesses use it
- Internal knowledge assistants for SOPs and HR policies
- Customer support over help-centre articles
- Contract and compliance search
- Sales enablement over product documentation
Common pitfalls
Poor document parsing, no access control, and no evaluation are the usual reasons RAG projects disappoint. Measure answer accuracy on real questions before launch.
Want to see RAG on your own documents? Coder97 can build a proof of concept in a few weeks.