Playbook
Retrieval Quality Loop Playbook
Improve RAG answer quality through corpus hygiene, retrieval diagnostics, and response-level evaluation.
Short answer
Improve RAG answer quality through corpus hygiene, retrieval diagnostics, and response-level evaluation.
Decision criteria
- Clean and segment corpus by authority and freshness.
- Instrument retrieval diagnostics: hit rate, chunk relevance, and latency.
- Introduce weekly eval sets with human-in-the-loop scoring.
Who this is not for
- Teams without an owner for execution and rollout accountability.
- Organizations that cannot measure outcomes from this workflow.
- Programs that cannot support regular quality reviews.
Proof points
- Higher grounded-answer precision on priority queries.
- Lower hallucination incidence in audited samples.
- Improved task completion without support escalation.
When to Use This
- Answers look fluent but regularly miss source-grounded details.
- Teams cannot explain why quality drops across segments.
- Evaluation is anecdotal and not tied to business outcomes.
Workflow
- Clean and segment corpus by authority and freshness.
- Instrument retrieval diagnostics: hit rate, chunk relevance, and latency.
- Introduce weekly eval sets with human-in-the-loop scoring.
Key Deliverables
- Retrieval diagnostics dashboard.
- Eval suite with versioned prompt/index baselines.
- Decision log connecting quality shifts to shipped changes.
How to Measure Success
- Higher grounded-answer precision on priority queries.
- Lower hallucination incidence in audited samples.
- Improved task completion without support escalation.
Next Step
We can adapt this playbook to your team’s current stack and operating constraints.
Diagnose retrieval qualityFAQ
Do we need to re-index everything immediately?
No. Start with high-value collections and progressively expand as diagnostics reveal bottlenecks.
What if source docs conflict?
Prioritize canonical sources, add recency rules, and expose citations so operators can adjudicate.
Can this be automated end-to-end?
Most of it can, but human review remains critical for edge-case governance and trust calibration.