Evaluate AI marketing output before putting it on a schedule

Define the decision the output must support

“Good marketing content” is too broad to test consistently. Choose a specific job instead: explain a software feature to a small-business operator, answer an implementation question, or turn a verified source update into an original practical guide. The evaluation should reflect the decision a reader needs to make, rather than whether the draft sounds fluent.

In an illustrative product example, a reader needs to understand which features remain available after a subscription ends. A successful answer states the boundary plainly, includes any dependency that matters and avoids promising access the product cannot provide. That gives you observable criteria. An impressive paragraph that fails to answer the question should fail the evaluation even if its grammar is excellent.

Build a compact set of difficult examples

OpenAI's evaluation guidance recommends evaluations tied to the actual task and informed by realistic examples. Start with a small set you can inspect carefully. Include ordinary questions alongside ambiguous inputs, conflicting source statements and requests for evidence that is not present. The set should reveal how the workflow behaves when producing an attractive answer would be easier than producing an honest one.

Keep each case independent of a preferred final wording. Record the question, allowed source facts, essential answer points and unacceptable claims. Add cases when a real failure occurs. This produces a useful history of the problems your application must avoid, rather than a collection of drafts selected because the model already handled them well.

Sources: [1]

Separate structural checks from editorial judgement

Some checks can be exact. An article either has a valid title, uses an allowed source URL and meets the required JSON shape, or it does not. Other checks require judgement: whether the explanation is understandable, whether an example is helpful and whether a qualification changes the reader's decision. Do not combine these into one unexplained score.

Use a review template with four distinct fields: factual support; relevance to the reader's task; practical usefulness; and format validity. Add a short reason for each result. A single unsupported product claim can justify rejection even when the remaining fields pass. Averages should not conceal a failure that makes publication unsafe or misleading.

Sources: [1]

Test uncertainty and unsupported claims deliberately

Ask the workflow a question whose answer is absent from the sources. A useful response acknowledges the missing evidence and explains what would need checking. It should not manufacture a price, performance result or customer quotation to complete the article. Test the same boundary with source material that contains promotional language or instructions unrelated to your task.

Google's helpful-content guidance emphasises original value and reliable presentation. Apply that editorial standard to your own examples. Label a hypothetical campaign as illustrative, explain the assumptions and keep numerical outcomes out unless a legitimate source supports them. An example can be concrete without pretending that your company ran the campaign or observed a particular result.

Sources: [2]

Make evaluation part of every meaningful change

Save the prompt version, model identifier, source snapshot references and validation outcome for a test run. When you change the workflow, compare its behaviour on the same cases. Also inspect genuinely new examples so that improvements do not merely reflect familiarity with a fixed set. Record what got better and what still fails in ordinary language.

Before scheduling, test the non-writing paths as well: an unavailable source, malformed output, a provider timeout and a duplicate daily job. The expected behaviour may be to publish nothing and preserve a clear reason. A useful automated workflow does not need to succeed on every input. It needs to make its successes defensible and its failures recoverable without silently creating misleading content.

Sources: [1]