AI evaluations (evals)

01why it matters for a business

Evals are how you replace it seems to work with evidence. AI systems are probabilistic and sensitive to small changes; a prompt tweak that fixes one case can quietly break five others, and a model update from a vendor can shift behavior overnight. Without a test set, every change is a gamble and every vendor claim is unverifiable.

For executives, evals are also the basis for go and no-go decisions. Before launch, they answer whether the system is accurate enough on the cases that matter. After launch, they show whether quality is holding. They make vendor comparisons concrete: run both candidate models on your eval set and compare accuracy, cost, and speed side by side.

02what it looks like in practice

A company building an AI assistant for insurance claims intake collects a few hundred past claims with the correct classification, coverage questions, and missing-information flags, including tricky and borderline cases. Every change to the system runs against this set, and a dashboard shows accuracy by claim type. When the team considers switching to a cheaper model, the eval shows it matches on most claim types but misses fraud-indicator flags more often, so the switch is made only for the types where it holds up.

03common mistakes

• Testing on a handful of hand-picked examples the system already handles well.
• No eval at all until something goes wrong in production.
• Using a model as judge without checking its judgments against people on a sample.
• Measuring one average score. Break results down by case type, because failures concentrate in specific categories.

04related terms

• AI hallucinationA hallucination is output from an AI model that is fluent and confident but false or unsupported: an invented fact, a made-up citation, a policy that does not exist, or a number that appears nowhere in the source.
• AI guardrailsGuardrails are the controls around an AI system that keep its inputs, outputs, and actions within acceptable limits: input filtering, output checks for policy, format, and sensitive data, limits on which tools and data it can reach, spending caps, and rules that route risky cases to a person.
• Human in the loop (HITL)Human in the loop is a design pattern in which a person reviews, approves, or corrects an AI system's output at defined points before it takes effect.
• Proof of concept vs productionA proof of concept (PoC) is a quick, limited build that shows an idea can work, usually on sample data without real users.

05where insomnia club fits