“Evaluate our AI” is a difficult brief to test against.
“Check whether the assistant chooses the applicable document when two versions conflict” gives an engineer a much clearer starting point.
We've launched the upgraded Acadify AI Lab website. To mark the launch, we're offering 8 hours of free Acadify AI Lab services, valid until 10 November 2026. Email contact@acadifysolution.com to discuss the work and scope for those hours.
The new site connects AI data and evaluation, engineering delivery and specialist talent. This article explains one way to prepare a focused technical brief before that conversation.
Name the decision
Start with the decision the work should inform. Are you reviewing a release, investigating a failure, comparing candidate models or preparing examples for a defined task?
The decision determines which evidence is relevant. A model comparison on unrelated prompts will not answer a question about your particular workflow. A coding tool's polished explanation will not establish whether its proposed patch satisfies the task.
Write the question before choosing a score. This makes it easier to identify what the work can establish and what will remain outside the scope.
Define a small task boundary
Use a fictional knowledge assistant as an example. The proposed task is to answer an employee's question using the applicable process guide. The fixture includes an old guide, its replacement and a question tied to the newer process.
Record the source relationship and the expected behaviour independently of the generated answer. Include what should happen if the available evidence cannot establish which document applies.
This is an illustrative fixture idea, not an executed evaluation or a claim about any model's accuracy.
Agree the evidence to inspect
The answer text is one output. Depending on the workflow, retrieved records, citations, tool actions or persisted state may matter too.
| Brief element | Question to answer |
|---|---|
| Task | What is the user trying to accomplish? |
| Inputs | Which approved records and conditions are available? |
| Expected behaviour | What would count as a correct or useful result? |
| Failure | Which omission, unsupported claim or action would matter? |
| Evidence | What should a reviewer inspect? |
| Limits | What will this scope leave untested? |
Adapt this to the system. A coding-tool task might need executable checks and developer correction notes. A data-preparation task might need provenance, permitted-use boundaries, a format specification and sample review.
Separate implementation from evaluation
Sometimes a review identifies work that belongs in the application. A retrieval issue, a confusing interface or an integration state may need an engineering change before a rerun is informative.
Keep the finding, proposed change and verification distinct. A recommendation is not an implemented fix. A passing synthetic fixture is not proof that every production case will behave correctly.
Acadify AI Lab's upgraded site provides routes to discuss both evaluation and engineering delivery, along with the expertise needed for the task. The scope should make clear which part you are asking for.
Prepare a useful first message
Describe the workflow, the uncertainty and the intended decision. Mention the stack or operational constraint when it changes the work. Start with a description rather than sending confidential source code or customer data.
Before any access is arranged, agree what information may be used, who needs it and what outputs should be retained. The site's approach makes scope, review and data permissions part of the engagement conversation.
Explore the launch and the offer
The upgraded site includes training data and expert demonstrations, human feedback, coding data, developer-tool evaluation, RAG and agent reviews, engineering delivery and specialist talent.
Visit ai.acadifysolution.com to explore. To discuss the 8 hours of free Acadify AI Lab services, email contact@acadifysolution.com and mention the launch offer. Valid until 10 November 2026. The specific work and scope are agreed through that conversation; the examples in this article are not guaranteed deliverables for every eight-hour engagement.
Acadify AI Lab is a practice of Acadify Solution.
If you were choosing one AI evaluation question to work on first, what would it be—and what evidence would change your next decision?
Disclosure: written by an AI agent for Acadify Solution / Acadify AI Lab. This is a promotional launch announcement with a fictional technical example, not a report of benchmark or client results.
Top comments (0)