RAG retrieves relevant passages from a document base and supplies them to the model for an answer. Quality depends more on document preparation, retrieval, permissions and testing than on the model alone.
RAG: building a reliable AI assistant on your documents
Prepare sources, manage permissions, cite passages and evaluate answers. Reliable results require connecting technology to a workflow, data, an owner and a measure. The following principles structure that decision.
01 — The knowledge base should contain approved, dated, deduplicated documents with an owner responsible for updates
The knowledge base should contain approved, dated, deduplicated documents with an owner responsible for updates.
02 — Chunking preserves titles, sections, tables and metadata needed to understand retrieved passages
Chunking preserves titles, sections, tables and metadata needed to understand retrieved passages.
03 — Permission filters apply before generation: the model must never receive content the user cannot access
Permission filters apply before generation: the model must never receive content the user cannot access.
04 — Answers show sources and acknowledge missing information instead of inventing
Answers show sources and acknowledge missing information instead of inventing.
05 — The evaluation set includes simple, ambiguous, conflicting, unanswerable and sensitive questions
The evaluation set includes simple, ambiguous, conflicting, unanswerable and sensitive questions.
Action plan
Use this sequence as a starting point. Each step should produce a decision or verifiable output before the next.
- Inventory sources
- Assign owners
- Clean and structure
- Configure permissions
- Create test set
- Track weak answers
Mistakes to avoid
- Importing the entire drive without review
- Hiding sources from users
- Measuring quality with three questions
Frequently asked questions
Not in most cases. RAG adds document context without retraining the model.
Yes, when permissions are integrated into retrieval and tested for each profile.
Key takeaway
RAG retrieves relevant passages from a document base and supplies them to the model for an answer. Quality depends more on document preparation, retrieval, permissions and testing than on the model alone.
The important point is to progress through evidence: a precise use case, representative test, documented limits and an outcome-based decision.