RAG Explained: Building a Knowledge Assistant Your Team Can Trust

Large language models are powerful, but on their own they don't know your policies, products or contracts — and they may fill gaps with confident guesses. Retrieval-augmented generation, or RAG, solves this by finding the relevant passages in your content first, then asking the model to answer using only those passages.

How RAG works, step by step

• 1. Ingest

  Documents from SharePoint, Google Drive, Confluence, help centers and other sources are collected and split into passages.

• 2. Index

  Each passage is converted into a searchable representation and stored with its source and permissions.

• 3. Retrieve

  When someone asks a question, the most relevant passages they are allowed to see are found.

• 4. Generate

  The model writes an answer grounded in those passages and cites them.

• 5. Evaluate

  Answers are tested against a set of known questions to measure accuracy over time.

What makes a knowledge assistant trustworthy

• Citations on every answer

  People can open the source and verify.

• Permission-aware retrieval

  HR, finance or legal content is never shown to the wrong people.

• Willingness to say 'I don't know'

  If the sources don't contain an answer, the assistant says so.

• Content hygiene

  Outdated and duplicate documents are removed or marked so they aren't retrieved.