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Applied AI guide · 8 min read

How to Evaluate a RAG System Before Production

A practical evaluation model for retrieval relevance, grounded answers, citations, safety, latency and cost.
01

Build a representative question set

Include common questions, difficult edge cases, permission-sensitive requests and questions that should not be answered. Test against realistic source documents rather than a small showcase set.

02

Score retrieval and generation separately

Measure whether useful evidence appears in the retrieved context, then assess whether the answer is supported, complete and appropriately uncertain. This prevents a fluent response from hiding poor retrieval.

03

Set release thresholds

Define acceptable quality, citation, latency, safety and cost thresholds before launch. Failed cases need documented fallbacks, escalation and improvement ownership.

References and further reading

Official and independent guidance used to support this practical overview. Product capabilities and pricing can change; verify current provider documentation before making a final decision.

AI DISCOVERY ENGAGEMENT

Before investing in AI, identify the workflow worth funding first.

One focused engagement to assess your business workflows, compare 5–7 candidate opportunities and recommend the first workflow worth funding.

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