How to build an example set for testing an AI workflow

A folder of successful examples can teach a builder what good work looks like. It cannot, on its own, show what happens when the work gets messy. Build a test set around the variety of decisions your team makes. The most valuable examples are often the ones experienced staff double check.

Sort by the decision someone must make

For a distributor that processes orders by email, collect examples of a new order, a correction, a request for availability and a customer forwarding an old order as a reference. All four may mention the same products. Only some authorize a new order. Add unusual layouts, incomplete addresses and messages in the languages your team intends to support.

Ask a reviewer to label each example before the builder sees it. Keep the label specific: “create a draft with SKU A17 and quantity 4,” or “request confirmation because the current and quoted quantities conflict.” If two experienced reviewers disagree, record why and ask the policy owner to decide. That disagreement often points to missing business guidance.

Separate preparation from measurement

Keep related records together when you split examples into a development group and a reserved test group. Three forwarded versions of the same order should stay in one group. Otherwise the final test can reward recognition of a request the builder has already seen.

Remove personal details and commercial identifiers that are unnecessary for the decision. Where shapes or formats matter, replace them with realistic invented values. Store a source reference and label version in a restricted register.

Revisit the set when the business changes. If a new ordering channel becomes popular, collect examples from that channel and report its results separately. Mixing them into the original batch can hide a new weakness and make it hard to tell whether the original workflow still behaves as expected.

Put this into practice

Bring a sample of the work and the tools your team uses. We can help define a first project, who reviews it, and how to tell if it works.

Explore AI implementation · Discuss your project with Escencion