Design a RAG release report for a reviewer

A reviewer needs enough information to reproduce the evidence, inspect failures and understand what is still untested. A dashboard score without corpus, configuration and gates is not a release report.

Keep review status explicit

The retrieval evaluation lab builds an authored report record.

from retrieval_cases import release_report_case

result = release_report_case()
assert result["sections_complete"] is True
assert result["approved"] is False
assert result["report"]["reviewer"] == "pending"
assert result["report"]["generation"] == "not executed"

The report contains corpus, index, task set, retrieval result, generation status, security test, known limits, decision and reviewer. Complete sections do not create approval.

Lead with scope and decision

State intended users/action, candidate version and recommendation. Link exact source/index/prompt/model configurations and evaluation dataset. Separate retrieval, answer support, citation, abstention, latency/cost and security results with denominators.

Include per-slice and critical-gate status, notable regressions, hard negatives and unresolved disagreements. Show one trace for a success, an unsupported question and a failure. Link artifacts instead of pasting only rounded numbers.

Record limits and operations

Name synthetic data, missing languages, unexecuted components, local-only timings, pending access review and absent load tests. Define deployment plan, monitoring, owner, rollback and reevaluation triggers. A report based on offline fixtures should recommend a controlled live evaluation, not production release.

Preserve reviewer comments and final disposition separately from automated checks. Version the report with the complete system; do not edit it after release without a new record.

The Generative & Agentic AI course uses this report as the evidence bridge into LLMOps and capstone defence.

Exercise

Write a report for one candidate and give it to a reviewer with no verbal context. Record every question they cannot answer from linked evidence, repair the report and retain approval as pending until an authorized review occurs.

Continue learning

This article is part of the Retrieval quality and grounded answers sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Build a retrieval regression suite.
• Continue with Portfolio project: a cited policy assistant with abstention.

Reference: NIST AI RMF playbook.