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Everyone wants the same thing from internal AI: ask a question in plain language, get an accurate answer from our information, not the internet's. The technique that makes this work is retrieval-augmented generation, or RAG, and the idea is simpler than the acronym.
Instead of relying on what a model happens to have memorised, RAG does two steps. First it retrieves the passages from your own documents most relevant to the question. Then it asks the model to answer using those passages, and to cite them. The model becomes a careful reader of your content rather than a guesser.
That is why a good RAG assistant can answer “what is our refund policy for enterprise customers?” with your actual policy and a link to the document, rather than a plausible-sounding invention.
The quality of a RAG assistant is mostly the quality of what you feed it. Clean, current, well-structured documents produce good answers; a pile of contradictory PDFs produces confident nonsense. Practical groundwork includes:
Garbage in, confident garbage out. RAG rewards teams that tidy their knowledge first.
The single most important design choice is to show the sources behind every answer. Citations let people verify, build trust, and quickly spot when the assistant has drifted. An answer-bot that cannot tell you where its answer came from should not be trusted with real decisions, a principle that connects directly to human-in-the-loop oversight.
A knowledge assistant must honour your existing access controls. It should retrieve and answer only from content the specific user is already entitled to see. Bolting access control on afterwards is risky; designing it in from the start is the only safe way. This is exactly the kind of guardrail we build into every deployment.
The highest-value uses are wherever people repeatedly hunt for answers buried in documents:
A knowledge assistant is often the ideal first agentic project, bounded, high-value, and forgiving, and it pairs naturally with AI support triage. If you want one grounded in your documents, with citations and proper permissions, our AI team builds them. Start with a free assessment.
It dramatically reduces it. By forcing the model to answer from retrieved passages of your real documents, and showing the sources, you ground responses in fact. It is not a guarantee, which is why citations and a feedback loop matter, but it is the difference between a useful tool and a confident liar.
Only if you let it. A properly built assistant respects your existing permissions, retrieving and answering from content the specific user is already allowed to access. Wiring in that access control is a core part of the build, not an afterthought.
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