Ask a general AI model about your company's refund policy and it will produce a confident answer that has nothing to do with your policy. It has never seen it. Retrieval-augmented generation, RAG for short, fixes this in a simple way: before the model answers, the system finds the relevant passages in your own documents and hands them to the model to answer from.
The model stops being the source of the facts. It becomes the reader that turns the right passages into a clear answer.
01The three steps
- Prepare. Your documents are split into passages and indexed by meaning, so a passage can be found even when the question uses different words.
- Retrieve. When someone asks a question, the system finds the handful of passages most likely to hold the answer.
- Answer. The model writes a reply using only those passages, and the system shows where each part came from.
02Why it beats searching by keyword
Keyword search finds documents that contain your words. A question such as "which contracts renew next quarter" contains none of the words in the clause that answers it. Search by meaning finds the clause; the model then reads it and answers the question that was asked.
03Why it beats training a model on your data
- Documents change. An index is updated in minutes; a trained model is not.
- Sources can be shown. Every answer points to the passage it came from.
- Permissions can be kept. The system only retrieves what the person asking is allowed to see.
- Nothing is baked in. Remove a document and it stops being used.
04What separates a good system from a demo
- It says "not found" when the documents do not contain the answer, rather than filling the gap.
- It respects the access rules you already have, document by document.
- It handles scans, tables and drawings, not only clean text.
- It shows the source every time, so a person can check in one click.
- It is measured: a set of real questions with known answers, re-run whenever something changes.
05What it needs from you
The documents, in whatever state they are in, and a few people who know which questions matter and what a right answer looks like. The second part is the one most often skipped, and it is what turns a clever demo into something staff rely on.
In short
- RAG finds the relevant passages in your documents first, then has the model answer from them.
- It works with changing documents, shows its sources and can honour existing permissions.
- A good system admits when the answer is not in the documents.
- It should be tested against real questions with known answers, not judged by a demo.
Questions
Is RAG the same as a chatbot?
No. A chatbot is an interface. RAG is the method behind a useful one: it decides what the model is allowed to read before it answers.
Can RAG run without sending documents to an outside service?
Yes. The index and the model can both run on your own servers, so documents and questions stay inside your network.
Does it work on scanned documents?
It can, once the text has been read from the scans. Quality depends on the scans, and a good system flags pages it could not read reliably.
What does Quantum Beetle build here?
MNEMOS is built to answer questions from an organisation's own documents with the source shown. A single Enterprise Search Layer can also be added on its own.
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