How RAG works, plainly
RAG connects a language model to your content. When someone asks a question, the system first searches your documents for the most relevant passages, then gives those passages to the model to answer from — with links back to the source.
The result is an assistant that speaks about your business specifically and can show its work, instead of a general model improvising. Done well, it can also say 'that isn't in our documents' rather than making something up.
- Answers grounded in your documents, not the open internet
- Every answer can cite the source passage it used
- Update the knowledge base and answers update — no model retraining
- Can restrict answers to only what's in your content
- Abstains when the answer isn't in your data
Building a RAG system that stays accurate
Retrieval quality is where RAG projects succeed or fail. Getting it right means chunking documents sensibly, choosing good embeddings, and testing against real questions from your team — not just a happy-path demo.
We also handle the unglamorous parts: keeping the index fresh as documents change, respecting who's allowed to see what, and measuring answer quality honestly so you know it's working.
- Sensible document chunking and quality embeddings
- A vector store sized to your content and query volume
- Permission-aware retrieval so users only see what they're allowed to
- A refresh process to keep the index current
- Evaluation against real questions, with a human-review step where needed
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Frequently asked questions
Is RAG the same as training a model on our data?
No — and that's usually a good thing. RAG retrieves your documents at question time instead of baking them into the model, so updates are instant, sources are citable, and you avoid the cost and risk of fine-tuning. For most business knowledge, RAG is the right tool.
Can it be restricted to only answer from our data?
Yes. We can constrain the assistant so it answers only from your content and explicitly says when something isn't there, rather than falling back to general web knowledge or guessing.
How does it handle documents different users shouldn't all see?
We build retrieval to respect permissions, so the system only pulls passages the asking user is allowed to access. Access control is part of the design, not an afterthought.