Academic review calls for separate retrieval and answer evaluation

The customer concern is a persuasive dashboard that does not explain whether an assistant answers correctly. Include process owners, evaluation staff and procurement. Ask what ground truth exists, who can judge difficult cases and whether the buyer needs retrieval diagnosis or service acceptance. Offer a limited evaluation-design engagement for one knowledge workflow. The value hypothesis is a more defensible decision and clearer remediation priorities. Do not market the framework as a validated accuracy improvement or claim the paper proves a fault in a particular NVIDIA product.

Build an evaluation contract containing queries, approved reference evidence, generated answers and retrieval traces. Pin judge versions and prompts, compare automated decisions with human labels and isolate ambiguous items for review. Prerequisites include trusted reference answers, controlled test access and reproducible component settings. Keep production identities and sensitive records out of external judging unless explicitly approved. Test retrieval and generation independently before diagnosing an end-to-end score change. The proof of value is explainable disagreement and a repeatable local measurement process, not a universal quality certificate.

Assign an evaluation owner independent of feature delivery to maintain rubrics and review disputed cases. Implement dataset versioning, periodic spot checks and a documented escalation path with domain experts. Dependencies include annotation time, privacy review and storage controls. Train staff to distinguish fluent answers from supported answers and include users with accessibility needs in workflow acceptance. Proposed acceptance is traceable evidence for each scored case, reproducible results within an agreed tolerance and reviewed disagreements before release. Risks include aging references, reviewer inconsistency and evaluation cost becoming an unfunded operational dependency.