Turn AI Output into a Usable Data Contract

A practical approach

Step 1

List the smallest set of fields the next step actually needs. Give each field a type, allowed values and a rule for missing information.

Step 2

Validate the returned structure and apply domain checks. A date-shaped string is not enough if the date must fall within a particular event window.

Step 3

Choose a controlled failure path: request clarification, queue review or return a clear error. Limit repair attempts so malformed outputs cannot loop indefinitely.

Worked example

An enquiry extractor returns name, requested_topic and preferred_date. A customer says ‘sometime next month’ without giving a day. Rather than manufacture a calendar date, the record stores a null date and a clarification question. The booking system remains responsible for availability and confirmation.

A mistake to avoid

Silently replacing invalid values with convenient defaults makes downstream reports look complete while hiding uncertainty. A default such as ‘beginner’ may change which course a learner is offered. Preserve the distinction between unknown and explicitly provided.

Try it yourself

Design a five-field extraction contract and feed it three incomplete messages. Write the validation outcome for each field before running the model, then compare the actual result with your expected record.

Your implementation checklist

• Required and optional fields are explicit.
• Unknown is different from an empty string.
• Domain rules run outside the model.
• A failed response has a bounded recovery path.