If three documents disagree, the model picks one and sounds certain.
Retrieval-augmented generation lets an agent answer from your manuals, contracts, policies and tickets rather than from the open internet — with a citation on every answer. The engineering that matters is deciding what goes in and getting retrieval to find the right passage.
In short: RAG means the agent searches your documents first and answers only from what it found, quoting the source. Done properly it’s the most reliable way to put a model in front of internal knowledge. Done carelessly it becomes a confident narrator of your out-of-date files.
“All our documents” is the wrong answer
It’s the most requested version of this project and the one that most often disappoints. Point a retrieval system at a shared drive and it will happily find the 2021 policy, the draft nobody approved, and the version somebody saved as FINAL_v3_use_this.
So the first phase is deciding what is allowed in. Which documents are current, which contradict each other, which have an owner who will keep them updated, and which should be excluded entirely. That audit is unglamorous, and it is the difference between an agent people trust and one they stop using in month two.
Four things that decide answer quality
How documents get split
Cut a document at arbitrary lengths and you separate a rule from its exception, so retrieval finds one without the other. Splitting on real structure — sections, clauses, headings — is dull work that improves answers more than switching models does.
Whether retrieval finds the right passage
Most wrong answers are retrieval failures, not generation failures — the model summarised faithfully from the wrong paragraph. We test retrieval separately from answering, because they fail for different reasons and need different fixes.
What happens when sources conflict
The honest behaviour is to say the sources disagree and show both, not to quietly pick. That surfaces contradictions in your documentation instead of hiding them — which is worth having on its own.
Who is allowed to see what
Permissions have to survive retrieval. If a document is restricted, the agent must not summarise it for someone without access — a leak through an answer is still a leak. Permissions get enforced at retrieval time, not filtered afterwards.
Every answer carries its source
Not as decoration — as the mechanism that makes the thing usable. A citation lets the reader verify in one click, gives them the document they’ll need anyway, and makes it obvious when the agent has drawn on something outdated. Answers it can’t ground in a source don’t get given; it says it doesn’t know and logs the gap.
What we won’t do
Index a drive nobody maintains
If no one owns the content, no one owns the wrong answers either. We’d rather start with one department’s genuinely current material and widen from there.
Let it answer without a citation
An ungrounded answer in a system people believe is grounded is worse than no system. Below the confidence threshold it declines and flags the gap.
Recommend fine-tuning instead
For factual internal knowledge, retrieval beats training almost every time — it updates when the document updates, and it can cite. Fine-tuning has its uses; this generally isn’t one.
How it starts
With fifty real questions your people actually ask, and the documents that should answer them. We build a test set from those, measure whether retrieval finds the right passage before worrying about how the answer reads, and report both numbers. That set stays as the regression check every time the content or the model changes.
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Which questions does your team keep asking each other?
Send us fifty of them and the documents that should hold the answers. We’ll tell you what state the source material is in before quoting anything — that assessment is usually the most useful part.
An agent that searches your own documents first and answers only from what it retrieved, quoting the source. It is the most reliable way to put a language model in front of internal knowledge, because the answer is tied to a passage someone can check rather than to whatever the model remembers.
Can we just point it at all our documents?
That is the most requested version of this project and the one that most often disappoints. Retrieval will happily surface the 2021 policy, the unapproved draft and the file saved as FINAL_v3. The first phase is deciding what is allowed in — what is current, what contradicts what, and which documents have an owner who will keep them updated.
Why do RAG systems give wrong answers?
Usually because retrieval found the wrong passage, not because the model wrote badly — it summarised faithfully from the wrong paragraph. That is why we test retrieval separately from answering. The other common cause is chunking: splitting documents at arbitrary lengths separates a rule from its exception, so one gets found without the other.
Will it respect document permissions?
Yes, and they are enforced at retrieval time rather than filtered afterwards. If someone lacks access to a document, the agent must not summarise it for them — a leak through an answer is still a leak. This gets tested adversarially before launch.
Is RAG better than fine-tuning a model on our data?
For factual internal knowledge, almost always. Retrieval updates the moment the document updates, and it can cite its source; a fine-tuned model cannot do either and needs retraining whenever a policy changes. Fine-tuning has genuine uses, but answering questions from a body of documents is generally not one of them.
