A product team should measure model or system quality separately from whether the workflow reaches its defined completion state. A fluent, well-supported response does not prove that the work was completed, while a completed workflow does not prove that the response was accurate, relevant, or safe.
The cited evaluation guidance distinguishes quality metrics such as coherence and fluency, retrieval-specific measures such as groundedness and relevance, safety and security, and task completion. Each answers a different question and should remain visible in the evaluation.
Give each measure one job
| Evaluation layer | Core question | Measures | Evidence to inspect |
|---|---|---|---|
| Output quality | Is the response coherent and fluent? | Coherence, fluency | Generated output |
| Retrieval quality | Is the answer supported by the retrieved context, and is that context relevant? | Groundedness, relevance | Response and retrieved context |
| Safety and security | Did the system satisfy the defined safety and security checks? | Safety, security | Test outcomes and observed failures |
| Workflow outcome | Did the work reach its defined completion state? | Task completion | Workflow status and completion record |
This separation prevents a system-level problem from being mislabeled as model quality. For example, an unsupported answer may reflect poor retrieval rather than the generation model itself.
Define completion before judging the model
“Completed” must have an operational meaning. The team should specify:
- Which event or state marks completion
- Which cases are eligible for completion
- What counts as failure, rework, or abandonment
- Whether a response alone counts as completion
- Where the completion status is recorded
The definition should describe the work, not merely the appearance of an answer. Otherwise, a plausible response could improve a completion metric without completing the intended task.
Evaluate both layers on the same cases
Representative workflow cases should be assessed at both layers. For each case, the team can record the output-quality findings, any retrieval findings, separate safety and security results, and the final workflow status.
Keeping these records connected makes mismatches easier to inspect. It also avoids collapsing unlike measures into one score before the team knows what each score contributes.
Investigate disagreements between the scores
Different combinations point to different areas for further checking:
- Good output quality but incomplete workflow: The response alone does not explain the outcome. Inspect retrieval, instructions, interfaces, tool use, handoffs, and process requirements. -Poor output quality and incomplete workflow: Review both the generated result and the workflow path rather than assuming one component caused the failure.
- Completed workflow but weak output quality: The completion status may conceal a grounding, relevance, fluency, safety, or security problem.
- Strong results in both layers: This supports the selected measures for those cases, but it does not establish that every workflow condition is covered.
These combinations are diagnostic leads, not proof of causation.
Confirm the measures in the actual product context
Before relying on the results, the team must confirm that the metric definitions match the intended user goal, the evaluation cases represent real workflow conditions, reviewers apply labels consistently, and logs connect the response, retrieval context, actions, and completion state.
The team must also establish its own thresholds and investigate alternative explanations for the observed results. The cited guidance supplies distinct measure categories and emphasizes clear metric definitions and performance data; it does not establish a universal passing threshold or prove why a particular workflow succeeded or failed.