1 minute readReference note
How should an ORM team choose search queries and AI prompts for testing?
In brief
A useful AI or search question set reflects actual research tasks: discovering options, checking a shortlist, verifying a claim, and investigating a concern. It should be informed by evidence such as customer questions, search data where available, support issues, procurement requirements, and subject-matter knowledge. Internal preferred wording alone can omit the questions that expose real gaps.
The design should separate observed audience behavior from editorial judgment. Rare questions can still deserve inclusion when they carry serious consequences, but they should not be represented as high-volume demand without evidence. Known-brand, category, comparison, and sensitive-issue tests also need separate labels because they describe different encounters. Include former names, key products, leadership, and markets where they create distinct research paths.
Stable core questions support trend comparison, while a documented exploratory set discovers new problems. When wording, platforms, or collection conditions change, the report should retain the distinction. The practical output is a sample that can identify specific source and asset work, with enough coverage to support its stated conclusion. A small sample can be excellent for one known error and weak for a broad assessment; the scope of the claim should match the scope of the test.