Evals drive the next chapter in AI for businesses (openai.com)

OpenAI is pushing “evals” — structured tests and iterative measurement processes — as the next operational tool for companies using AI, releasing a practical primer for business leaders. They argue that while frontier evals measure raw model capabilities, the real business value comes from contextual evals: bespoke assessments tied to specific workflows and goals (e.g., converting inbound emails to demos). Evals make vague objectives explicit, surface high-severity errors, and create a measurable path to higher ROI by turning expert judgments into living “golden” example sets and error taxonomies that guide product readiness and ongoing improvement. The primer lays out concrete practices: form a small cross‑functional team, define success criteria per workflow step, prototype and run error analyses on 50–100 outputs, build a test environment that mirrors real conditions, and use rubrics and LLM graders with human audits. Operate a data flywheel (log inputs/outputs/outcomes, sample hard cases, route to expert review, and incorporate judgments back into evals, prompts, or models). Evals complement A/B testing, demand continuous maintenance as models and goals change, and can create a hard‑to‑replicate, context‑specific dataset that compounds competitive advantage. Crucially, OpenAI frames this as as much a management discipline as a technical one: defining “great” is the essential first step.