Your AI assistant gives a convincing answer during a demonstration. Before employees use it to interpret service policies, prepare quotations or answer customers, ask a different question: how does it perform on cases the demonstrator did not choose?

A useful pilot needs agreed questions, authoritative reference material and a consistent way to review answers. For UAE teams working across departments or languages, the test should reflect the actual tasks and documents involved. This guide proposes a practical acceptance process for a knowledge assistant; it does not claim that a good score proves every future answer is safe.

The illustration is a conceptual answer-review workbench, not an authentic AI product interface or an ITZ client result.

Choose a bounded business task

Begin with something reviewable, such as helping staff locate an approved delivery-policy clause. Name the document owner, intended users and permitted decisions. An assistant that locates a clause has a different risk profile from one authorised to promise a refund or change a customer record.

Define the expected behaviour when evidence is missing: ask a clarifying question, identify uncertainty or refer to a named human process. Do not let the pilot quietly expand into approving exceptions. The AI action-boundary guide addresses tool use and untrusted instructions separately.

Create a reference sheet before collecting impressive examples

For each case, record the question, the current approved document and section, the required answer points, unacceptable claims and the expected escalation when necessary. Have the business owner approve that sheet. Test data should be authorised and minimise personal or confidential information.

OpenAI's evaluation guidance recommends task-specific tests, representative and difficult cases, explicit success criteria and human review of automated scoring. Those principles can be used without adopting a particular evaluation platform. Avoid importing a vendor's sample numerical target as your own business acceptance threshold.

A focused AI integration and business knowledge-assistant review can turn the business task into a testable brief. Plan Your AI Answer Quality Pilot with the intended audience, approved information sources and decisions that must remain with a person.

Include cases where a confident answer would be wrong

The following is a proposed test mix, not an industry benchmark or a measured ITZ result:

  • Ordinary question: the answer is present clearly in an approved document.
  • Missing detail: a branch, product or service date is needed before the question can be answered.
  • Conflicting material: an obsolete draft and an approved current policy appear similar.
  • No supported answer: the documents do not authorise the requested promise.
  • Different wording: employees ask the same question with abbreviations or indirect phrasing.
  • Language boundary: if Arabic and English are in scope, a competent reviewer checks each language and the meaning of key terms.

For example, a synthetic customer asks whether an expired promotion can still be honoured. If the approved material gives no exception authority, a fluent invented extension is a failure. A response that identifies the missing authority and follows the agreed escalation can be correct even though it does not give the customer a yes.

Score evidence and behaviour separately

Use a short review rubric: factual correctness, support from the cited passage, completeness of required points, appropriate uncertainty and compliance with the task boundary. Define examples of a pass and failure before reviewers start. Record disagreements and resolve ambiguous reference answers.

A citation is useful only if the cited material supports the claim. Have a reviewer open it, check the relevant passage and verify that it is the approved version. Evaluate retrieved material as well as the final wording: the assistant may write badly from good evidence or write confidently from the wrong evidence.

Keep severe errors visible rather than hiding them inside an average. A missed source link and an invented contractual commitment should not receive the same treatment. The business owner should decide which failure types block wider use.

Retest changes without teaching to every answer

Google’s evaluation guidance highlights diverse, application-specific test data and held-out cases. Keep some approved cases aside for an independent check after adjustments. Record the assistant configuration, document snapshot and date with the result. Revisit representative questions across repeated runs; a single favourable response does not establish dependable behaviour.

When a prompt, model, retrieval configuration or source document changes, rerun the relevant checks. OpenAI's guidance treats evaluation as a continuing process and calls for calibrating model-based grading against human judgement. Automated scores can help organise a larger review, but the accountable business reviewer still needs to examine consequential failures.

Access remains a separate acceptance gate. The document-permissions guide explains why answer quality does not establish permission to see the underlying content. Start with limited access and human review; expand only when both the information boundary and task performance have been checked.

Plan Your AI Answer Quality Pilot to discuss the use case, reference answers, evidence checks, escalation rules and release decision. Bring a description of approved sources rather than confidential records to the enquiry form.

Official evaluation guidance reviewed 23 October 2026. The rubric and examples are proposed pilot methods. No accuracy rate, client outcome or productivity saving is claimed.