Beyond Accuracy: A 5-Step Framework for Meaningful AI Evaluation (oblsk.com)

A five-step framework urges teams to move AI evaluation beyond accuracy metrics and align testing with concrete business outcomes. Rather than treating accuracy or fluency as ends in themselves, practitioners should start by defining strategic purpose (what problem the model must solve and for whom) and then identify which parts of the output users actually value. This reframes evaluation around measurable business signals — e.g., match quality and transaction completion for a matchmaking system, response rates and meetings booked for a sales assistant, or resolution rates and satisfaction for a chatbot — instead of generic technical benchmarks. The framework’s technical guidance continues with explicit credibility criteria (citations, tone, domain precision), a manual, pattern-focused error analysis to surface input-output failure modes, and finally translating those insights into evaluation design that measures outcomes (match quality, user trust score, transaction completion) rather than just correctness. Practical implications: build tests that reflect user behavior, use error-pattern recognition to simplify or harden inputs, and prioritize trust markers that drive adoption. The result is an evaluation loop that connects model performance to strategic value, turning AI from a lab experiment into a measurable business driver.