1. RAG is not magic search
Retrieval-augmented generation combines document retrieval with generated answers. The quality of the response depends on what was retrieved, how documents were chunked, whether sources are current and how the answer is reviewed.
Poor documents create poor answers, even with a strong model.
- Prepare documents before indexing them.
- Remove duplicates, obsolete files and ambiguous drafts.
- Keep document titles, dates and owners visible.
- Prefer answers that cite retrieved sources.
- Treat missing answers as documentation gaps.
2. Permissions and boundaries matter
A company knowledge base may include HR, finance, customer, technical and operational content. Not every user should retrieve every document. RAG architecture must respect access boundaries.
This is as much an information governance issue as it is a technical design.
- Classify document collections by sensitivity.
- Apply role-based access to retrieval where needed.
- Avoid indexing secrets, credentials and private keys.
- Log high-impact usage for audit and improvement.
- Separate internal assistant use from public website content.
3. A feedback loop makes RAG better over time
The first version of a RAG assistant will expose missing, outdated or confusing documentation. That is useful. Each failed answer should become a source improvement task.
The system becomes more valuable when content maintenance and AI evaluation work together.
- Collect unanswered or low-confidence questions.
- Review incorrect answers and update sources.
- Track which documents are retrieved most often.
- Add human approval for sensitive answer categories.
- Measure whether users resolve tasks faster with the assistant.
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