# Evaluate the application, not just the model.

> A model score from a public benchmark may have little relationship to whether a specific employee can complete an approved workflow or whether a product user will trust a generated answer.

Canonical URL: https://marmation.com/expertise/model-evaluation/

## The decision

Which failure would stop you from shipping?

Make that failure a release case with an expected behavior, a reproducible setup and a named review responsibility. A public model score is not an application's acceptance decision.

## The situation behind the request

A team changes a model version. Offline accuracy improves, but the assistant begins answering with less specific references and spending more time on requests that should return “not enough evidence.”

## Where the obvious solution falls short

Generic judges and happy-path prompts can miss permission leakage, extraction errors and user-interface failure. Comparing only model outputs ignores changes in retrieval and tools.

## How we would approach the work

Define task-specific acceptance cases, make failure categories visible and hold a representative regression set. Include retrieval evaluation, answer evidence, authorization, latency, cost and human review.

## What the engagement may include

- Evaluation taxonomy and representative datasets.
- Reproducible harness, prompt/model/source version control.
- Human calibration for automated evaluator scores.
- Quality gate and monitoring/rollback plan.

## The choices worth making explicitly

Automation can broaden test coverage but cannot replace domain and security judgment. Some tasks need deterministic expected outputs; others require expert review.

## What we would test before calling it ready

Include false positives, no-answer, stale sources, adversarial inputs, cross-tenant access and upgrade regressions. Review not only average performance but costly tail failures.

## A sensible first step

Identify the failure most expensive to the buyer and make it a first-class acceptance case.

## Related reading

- [Autonomy should be earned, not assumed.](https://marmation.com/expertise/ai-agent-orchestration/)
- [Governance works best when it is part of the system design.](https://marmation.com/expertise/ai-data-and-model-governance/)
- [Change the delivery system, not just the editor.](https://marmation.com/expertise/ai-native-development-lifecycle/)