Control and assurance · International teams
AI evaluation and testing
Find out whether an AI workflow performs well enough for a specific job, including the cases where it should stop.
Understand the service
What this means in practice
AI evaluation measures a system against the task it is meant to perform. A useful test set includes ordinary examples, difficult examples and cases where the correct response is to refuse an action, ask a question or report missing evidence. A persuasive demonstration is not a substitute for repeatable tests.
Temrik can scope evaluation around an agreed workflow and acceptance decision. For a knowledge assistant, retrieval quality and answer support should be examined separately. For an action-taking workflow, permissions, duplicate handling and recovery matter as much as the quality of generated text.
- NIST: AI measurement and evaluation Measurement should reflect the technology and its use.
- Microsoft: retrieval-augmented generation Retrieval supplies context; source preparation and evaluation matter.
A practical workflow example
A company assistant answers twenty ordinary questions well but gives unsupported answers when a policy is missing. The proposed evaluation records that failure separately from retrieval success. The team then tests an explicit no-answer path and repeats the same cases after changing the configuration.
Proposed engagement
How we would approach the work
Build a representative set
Select authorised examples with expected outcomes and known failure cases. Keep a held-out set and record why the sample represents the intended use.
Use task-specific measures
Assess factual support, omissions, access boundaries and operator correction effort. Calibrate any automated scoring with human review.
Set release and retest rules
Agree acceptance criteria before judging the result. Repeat relevant tests after changes to prompts, models, connectors or source content.
Deliverables to agree in the scope
- A versioned evaluation set and review rubric.
- A results report with failure categories and examples.
- A proposed release gate and regression-testing plan.
Access, sample information and reviewer availability affect the plan. Any implementation, provider costs, support arrangements and acceptance criteria are agreed before work begins.
Limits worth understanding
- Passing a finite test set cannot guarantee future accuracy or safety.
- A model scoring another model is an aid to review, not independent ground truth.
Questions to bring to the first conversation
- What would count as an unacceptable failure?
- Who can judge the expected answer or action?
- Which changes require the tests to run again?
International teams
Scope the work for your operating context.
For a system used internationally, segment results by language, region and task rather than relying on one average score. A strong result on common English questions may hide errors in local policy, dates or less frequent workflows.
A starting reference for your review: NIST: AI Risk Management Framework. Local obligations and deployment settings need to be assessed for the actual use case.
A focused next step
Work with Temrik.
Tell us about the workflow you want to improve and the outcome you need. We can review the context and discuss a focused assessment. Scope and price are agreed before paid work begins.