Understand AI Marketing
What Is an AI Evaluation?
Define an AI evaluation as a repeatable check against explicit criteria, examples and a recorded decision.
An AI evaluation is a repeatable check of an AI-assisted output against a purpose and explicit criteria. You look at the result, record whether it meets the criterion and explain what should happen next.
That is more precise than saying an answer “looks good”. It also does not mean that one check proves a model, prompt or workflow is reliable everywhere. The evaluation must fit the task, the risk and the decision you need to make.
What does evaluation mean here?
OpenAI describes evals as tests of model outputs against style and content criteria that you specify. In plain language, you decide what acceptable means, inspect the output against that decision and keep a record you could repeat.
For a small marketing task, the object being evaluated might be an article outline, a summary, a list of subject lines or a structured brief. The output does not have to be published. An intermediate output can still be evaluated if an error would travel into the next step.
An evaluation has three parts:
- Purpose: what the output is meant to help someone do.
- Criterion: the observable condition that counts as acceptable for this check.
- Decision record: pass, fail or uncertain, with a short reason.
Why “looks good” is not enough
Polish is not a criterion. A fluent answer may omit a required section, change an approved fact or introduce a claim no one supplied. A useful evaluation makes the judgement visible instead of relying on a general impression.
This does not replace human review. Human review asks whether the work is right for its purpose, audience, facts, privacy and use. An evaluation adds a repeatable question that can be applied to a recurring output.
Start with the purpose
Write one sentence about the job of the output. For example: “This outline should help a marketer draft a short educational article from approved notes.”
The purpose keeps the criterion relevant. If the task is an outline, a criterion about click-through rate is too distant. If the task is a factual summary, a criterion about visual style may be secondary. A criterion should help you decide whether this output is fit for its immediate next step.
Write one observable criterion
Make the first criterion small enough for a person to check. Useful forms include:
- includes the required sections from the approved brief
- answers the stated reader question
- keeps named facts tied to supplied material
- contains no unapproved product claim
- follows the agreed output format
Avoid criteria such as “is excellent”, “sounds human” or “will perform well”. They may describe a concern, but they do not tell a reviewer what evidence would count.
One criterion is enough for a starter exercise. A larger evaluation may need several criteria, but adding a long scorecard before you can make one clear judgement often creates work without clarity.
Choose a relevant example
The example should resemble the task you want to repeat. If you are checking article outlines, use an approved brief and a realistic outline request. Do not choose a convenient example that avoids the difficult part of the workflow.
For a first check, record the input, the instruction, the output and the criterion. If the input contains private or confidential material, use an authorised alternative or a labelled fictional example. The evaluation record should not make unauthorised information safe to share.
Record pass, fail or uncertain
Use a small decision vocabulary so another person can understand the result.
| Decision | Meaning | Example |
|---|---|---|
| Pass | The criterion is met in the checked output. | All three required sections appear and no unsupported product claim is present. |
| Fail | The criterion is not met. | One required section is missing, or an unapproved claim has been added. |
| Uncertain | The available evidence is not enough to decide safely. | The source note is ambiguous, so the reviewer cannot confirm whether the claim is approved. |
The short reason matters. “Pass” without a record of what was checked is difficult to learn from or repeat.
A fictional pass/fail example
ILLUSTRATIVE EXAMPLE: A marketer asks an AI tool for an outline from approved generic notes. The criterion is: “The outline contains the three required sections from the approved brief and adds no unsupported product claim.”
- Pass: The three sections are present, and every product detail is traceable to the notes.
- Fail: The outline has two sections and adds a feature not in the notes.
- Uncertain: The sections are present, but one product detail uses wording that the notes do not explain.
This is a constructed teaching example. No model was run, and it is not a client result or benchmark.
Keep quality separate from business impact
An evaluation can tell you whether a defined output criterion was met. It cannot, by itself, tell you whether the work generated a business result.
| What you check | What it can support | What it cannot prove alone |
|---|---|---|
| Required sections are present | The output follows that part of the brief. | The article will rank or convert. |
| Approved facts are preserved | The checked content stays within supplied material. | The facts are complete for every audience or situation. |
| Unsupported claims are absent | One quality or risk condition passed. | The workflow is reliable across all tasks. |
| Review time is recorded | An observation about this task and process. | A general productivity gain or return on investment. |
NIST notes that evaluation methods and measurements depend on context. Treat quality checks, workflow observations and business measures as different evidence types that may need different designs.
When does one evaluation help?
One evaluation can make the next decision clearer. It may show that a criterion is easy to check, that the output regularly misses a requirement or that the input is too ambiguous to judge. It does not validate every future output.
For a small test-and-improve loop, see How to Test and Improve an AI Marketing Workflow. That lesson covers the before point, one visible change and the next decision. U13 supplies the definition of the evaluation that sits inside that loop.
As tasks become more varied or higher risk, you may need more representative examples, several criteria, specialist review, documented uncertainty or a formal evaluation programme. A small marketing check should not be presented as a substitute for that work.
Your next step: write one criterion
Choose one recurring AI-assisted output. Write its immediate purpose, then one condition another person could inspect. Use one relevant example and mark the result pass, fail or uncertain with a reason.
If the result is uncertain, pause and identify what needs checking. If it fails, change the input, instruction or workflow only when you know what decision that change is meant to improve. Keep the evaluation record with the output so the next review has context.
Further reading
You Might Still Be Wondering...
Frequently asked questions
No. Human review is the broader person-owned decision about purpose, accuracy, relevance, privacy and usefulness. An evaluation makes one or more of those checks explicit and repeatable for a defined output or task.
No. Pass, fail or uncertain with a short reason can be enough for a starter check. Use a number only when it adds clarity and you can explain what it measures.
No. It shows that the criterion was met in the checked case. Other inputs, tasks, reviewers or conditions may produce different results.
Another person should be able to inspect the output and understand what would count as present, missing or uncertain. “Contains the three approved sections” is more observable than “is high quality”.
It can be part of a separate measurement plan, but it is not the same as checking output quality. Leads, rankings, conversions or time saved need their own definitions, data and context.