Retrieval-Augmented Generation helps reduce hallucinations and improves auditability—when implemented with the right chunking, ranking, and UX. Here’s a clear blueprint.
Start with the workflow, not the vector database
RAG is a tool, not a product. The best implementations begin with a workflow: “help support agents resolve tickets faster”, “answer policy questions with citations”, or “summarize contract clauses with sources.”
Once the workflow is clear, the retrieval system becomes a means to a measurable end: fewer escalations, faster resolution, higher confidence.
Chunking: the quiet determinant of quality
Chunking is where most RAG systems succeed or fail. Too small and you lose context; too large and you retrieve noise. The right strategy depends on document structure and user intent.
- Chunk by meaning (sections/headers), not fixed tokens only.
- Store metadata: doc title, section header, last-updated date.
- Include “breadcrumb” context so answers remain grounded.
Ranking and re-ranking: retrieval is a product feature
A strong baseline uses hybrid search (keyword + vector) and then re-ranks results for relevance. This is where reliability comes from—not just “better prompts.”
If you can’t explain why a chunk was retrieved, you can’t debug failures. Make retrieval transparent to engineers and support teams.
Make citations useful
Citations are not decorations. They should help a user verify and act. Link to the exact section, highlight the quoted text, and show document freshness.
- Show the source and last updated timestamp.
- Allow “open in context” so users can validate quickly.
- If retrieval is weak, say so and ask a clarifying question.
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