How to set up RAG for enterprise knowledge bases

Retrieval-augmented generation (RAG) only works if the plumbing is right. Here's a practical sequence for standing it up on an enterprise knowledge base.

1. Inventory your sources

List the systems that hold answers: policies in SharePoint, tickets in ServiceNow, product specs in Confluence. Decide which are authoritative and which are drafts to exclude.

2. Ingest and chunk

Pull documents on a schedule, strip boilerplate, and split them into passages of a few hundred tokens with overlap. Keep headings attached to the chunk so a policy clause never loses its section title.

3. Index with hybrid search

Store embeddings for semantic similarity and a keyword index for exact terms like SKUs, drug names, or policy codes. Hybrid search beats either alone.

4. Add metadata filters

Tag each chunk with region, department, document type, and effective date. At query time, filter before you rank so a US employee never sees the EU-only clause.

5. Retrieve, rank, and cite

Fetch the top passages, rerank them, and pass only the best into the prompt. Always return the source URL and section with the answer.

6. Evaluate retrieval separately

Build a test set of real questions with known-correct passages. Measure recall@k on retrieval before you blame the model. Most "hallucinations" are retrieval misses.

7. Refresh and monitor

Re-index on a cadence that matches how often the source changes. Log every query, retrieved chunk, and user feedback to catch drift early.

For the governance layer around this, see our Responsible AI-by-Design Framework or explore the Silverberry AI Platform.