One of the biggest changes in AI product development is the rise of evaluation as a daily practice. Instead of shipping prompts and hoping for the best, teams build evaluation suites—curated inputs, expected outputs, and scoring rules—that represent what “good” means for the product. Every change to prompts, retrieval, models, or tools gets measured against this suite. This turns AI development from an art into a disciplined loop: propose change → run eval → inspect failures → iterate. It’s how you move fast without breaking user trust.

Modern evals go beyond accuracy. They measure tone, policy compliance, citation correctness, consistency, hallucination rates, and business KPIs. For example, a customer support assistant might be evaluated on resolution rate, escalation rate, empathy, and adherence to policy. A document extraction system might be judged on field-level precision/recall and error severity. Teams also start using adversarial tests: tricky inputs that cause failures, regression tests for past incidents, and stress tests for long contexts. The result is a culture where “we improved the model” is not a claim—it’s a measured outcome.

This evaluation-driven approach changes team structure. You see “AI QA” roles, shared test datasets, and common scoring frameworks. You also see better collaboration between product, engineering, and operations because evaluation connects model behavior to real user outcomes. Over time, AI products will be won by teams with the best feedback loops: instrumentation in production, pipelines to convert user issues into test cases, and dashboards that show quality over time. The secret is not finding the perfect model; it’s building a system that learns from reality faster than competitors.