The problem RAG solves
Language models know a lot about the world but nothing about your pricing, policies or procedures. Asking them directly invites confident but wrong answers. RAG fixes this by giving the model the right information at the moment of the question.
How RAG works
- Ingest — documents are parsed and split into chunks.
- Embed — each chunk becomes a vector capturing its meaning.
- Retrieve — for each question, the most relevant chunks are found.
- Generate — the model answers using only those chunks, with citations.
Permissions matter
Retrieval must respect who is asking: an employee should never receive passages from documents they cannot access.
Measuring accuracy
Build a test set of real questions with expected answers. Measure retrieval quality, answer correctness and citation accuracy before launch and after every change.
Business use cases
- Internal policy and HR assistant
- Product and pricing assistant for sales teams
- Customer support knowledge assistant
- Contract and document Q&A
See our RAG knowledge assistant reference implementation and AI development services.
Conclusion
RAG is the most practical way to make AI useful with company knowledge — accurate, current and auditable.
