Evals and tests are repeatable checks that move you from “this feels fine” to “this passes the standard we agreed on.” A code test checks that a function returns the right answer. An AI eval checks whether a workflow pulls the right fields from ten sample transcripts or produces the expected structure. A guardrail says what shouldn’t happen; an eval checks the system actually behaves that way.
A scorecard on every client deliverable is the everyday version. You don’t ask “do I like it?” in a vague way. You check whether the numbers tie, the client’s name is right, the source is cited, and the format matches the firm standard. The scorecard makes quality less dependent on mood.
Why you care
Testing guards against fake progress. A new prompt may shine on one example and fail on five others; a new model may be smarter at reasoning and worse at your format. Save the examples that would prove the work is right, along with the expected answers, and rerun them whenever the system changes. AI work doesn’t improve just because the output sounds more confident. Evals are how you know it actually did.