A versioned evaluation set
Representative tasks, important exceptions, expected behavior, scoring rubrics, and coverage notes that describe what was not tested.
AI quality / AI quality checks & monitoring
Keep checking. Especially after a change.
Build a practical evaluation routine for an existing AI workflow, with representative cases, visible failures, and checks before model or source changes.
Discuss your project Start with an outline, not a perfect brief.
Where it gets stuckAI quality checks & monitoring,
AI quality checks & monitoring,
with the context attached.
An assistant that looked useful at launch can become less reliable as documents, users, and models change. A single average score can hide failure on the questions that matter most. The job is to connect evaluation evidence to an owner’s decision, not decorate a dashboard with an impressive percentage.
A good first scope
Choose one AI workflow and the decisions its outputs support. Bring a sanitized sample of ordinary tasks, known failures, and the changes you are considering.
The workflow
- 01 / The starting point
An internal policy assistant is being considered for a model update while its source handbook also changes. A versioned question set contains ordinary answers, exceptions, and questions it should decline.
- 02 / The right context
Run the baseline and candidate on the same approved cases, keeping model, prompt, and source versions separate. Score source support, completeness, refusal behavior, review burden, latency, and cost as relevant.
- 03 / Prepared for review
Prepare a comparison record and a failure queue grouped by task and severity. Show sample coverage and unresolved reviewer disagreements rather than presenting an unsupported overall quality claim.
- 04 / A person decides
The workflow owner reviews regressions and decides to keep, revise, or reject the candidate. The output includes monitoring and escalation rules; the evaluation does not deploy a model change.
Tangible work
A useful working handoff
A usable failure queue
Observed failures with source context, versions, severity, assigned owners, and a route for reviewer disagreements and false alarms.
A change-review routine
Repeatable baseline comparisons, contextual metrics, agreed escalation triggers, and the evidence an owner needs before approving a rollout.
A focused engagementDefine the job.
Define the job.
Test the handoff.
- 01
Define useful quality
Agree which mistakes matter for this workflow and who can judge them. Separate factual correctness, action safety, task completion, and review effort.
- 02
Measure with stable cases
Version the cases and score candidates consistently. Keep a separate challenge set to reduce the risk of tuning only to familiar examples.
- 03
Connect checks to ownership
Decide who reviews failures, when a change needs a fresh evaluation, and what warrants pausing or reverting an independently authorized rollout.
How we would startMap it. Test it.
Map it. Test it.
Then decide.
Agree the trigger, source systems, reviewer, and acceptance criteria. Build the bounded preparation path, then test it with the person who will own it.
Include the awkward cases
Test source drift, a new question category, a model update that improves typical answers but weakens refusals, and an automated evaluator that disagrees with the human reviewer.
Keep this boundary explicit
Metrics need task context, sample size, and limitations. Review the evaluation with the workflow owner before deciding on a production change.
See how acceptance evidence works ↗A few useful answers
Before we begin.
Which score should we track?
Choose measures tied to the actual job and review them by task type and severity. A single average can conceal failures on rare but important cases.
Can another AI judge the outputs?
It can help flag likely issues using an agreed rubric. Calibrate against human review and inspect disagreements; an automated judge can be inconsistent or share the same blind spots.
Does a passing evaluation authorize a model switch?
No. It supports an owner’s decision. Deployment, monitoring responsibilities, fallback criteria, and any production change need separate approval.