Evals, short for evaluations, are structured tests that measure how reliably an AI system does its job, instead of how well it happens to perform on the one example you just tried. They replace “this looks good” with “this is right nine times out of ten.” For agencies, evals are the line between an AI experiment and an AI process you can trust on a paying account.
Also known as evaluations, AI evals, model evals
Most people judge AI by gut feel. A few good answers and the tool feels trustworthy. A few bad ones and it feels broken. Neither tells you how the system actually performs across the range of inputs it will face in real work.
An eval makes performance measurable. You define what a good output looks like, assemble a set of real examples, run the system against them, and score the results. Now you have a number you can track, compare across tools, and watch over time. It is the same discipline as proofing work against a brief, except you run the same checks on every output rather than trusting whoever happened to review it that day.
If you cannot measure how well an AI step performs, you cannot responsibly put it in front of a client.
They separate toys from tools. Experiments live in the sandbox. Anything touching a real deliverable needs proof it holds up beyond a cherry-picked demo.
They catch regressions. Models and prompts change. Evals tell you when an update quietly made your results worse, before a client does.
They build internal trust. A team adopts an AI workflow faster when it can see the workflow scored against real examples rather than sold on a vibe.
An agency wants to automate first-draft meta descriptions for a client’s large e-commerce catalog. Before trusting it, the team builds a small eval: 50 real products, a clear rubric for what a good description includes, and a score for each AI output. The first prompt passes only 70 percent of the time. Two rounds of prompt refinement push it past 90 percent, and the team ships the workflow knowing exactly how often a human needs to step in. The decision rests on evidence, not optimism.
The workshop covers how agencies test AI workflows against real examples, so the steps you automate are the ones you have actually proven.