TL;DR
OpenAI has published a comprehensive primer on evaluation frameworks (“evals”) that enable business leaders to turn abstract AI objectives into consistent, measurable results. The framework follows a specify-measure-improve cycle, helping organisations decrease high-severity errors, protect against downside risk, and create a measurable path to higher ROI from AI investments.
Closing the AI Expectations Gap
Whilst over one million businesses worldwide leverage AI to drive efficiency and value creation, some organisations struggle to achieve expected results. OpenAI identifies evaluation frameworks as the critical missing element—methods to measure and improve AI systems’ ability to meet expectations.
Similar to product requirement documents, evals make fuzzy goals and abstract ideas specific and explicit. The company uses two types: frontier evals to measure model performance across domains, and contextual evals designed to assess performance within specific products or internal workflows.
The Three-Stage Framework
OpenAI’s evaluation approach follows a systematic cycle:
Specify: Define What “Great” Means Small, empowered teams combining technical and domain expertise define AI system purposes in plain terms. They create a “golden set” of example inputs mapped to desired outputs, establishing an authoritative reference of expert judgement. Early prototyping helps uncover failure modes through error analysis, creating a taxonomy of errors to track as systems improve.
Critically, this process is cross-functional rather than purely technical. Domain experts, technical leads, and stakeholders must share ownership—technical teams cannot judge alone what best serves customers or other departments.
Measure: Test Against Real-World Conditions Organisations create dedicated test environments mirroring real conditions, evaluating performance against golden sets under actual pressures and edge cases. Whilst rubrics help bring concreteness to judging outputs, traditional business metrics and newly invented metrics both play roles.
LLM graders—AI models that grade outputs like experts—can scale evaluation, but human oversight remains essential. Domain experts must regularly audit grader accuracy and review system behaviour logs. Measurement continues post-launch, with end-user signals built into ongoing evaluation.
Improve: Learn from Errors Continuous improvement addresses problems through prompt refinement, data access adjustment, and eval updates. A data flywheel logs inputs, outputs, and outcomes; samples on schedule; and routes costly or ambiguous cases to expert review. Expert judgements feed back into evals and error analysis, updating prompts, tools, or models.
This loop yields large, context-specific datasets that become valuable organisational assets—differentiated knowledge competitors cannot easily copy.
Strategic Implications for Leaders
OpenAI positions evals as the natural extension of measurement frameworks like OKRs and KPIs for the AI age. Working with probabilistic systems requires new measurement kinds and deeper trade-off consideration between precision, flexibility, velocity, and reliability.
“In a world where information is freely available and expertise is democratised, your advantage hinges on how well your systems can execute inside your context,” the company notes. Robust evals create compounding advantages and institutional know-how as systems improve.
Management Skills as AI Skills
At their core, evals require deep understanding of business context and objectives. OpenAI emphasises that if organisations cannot define what “great” means for their use case, they’re unlikely to achieve it.
This highlights a key lesson: management skills are AI skills. Clear goals, direct feedback, prudent judgement, and clear understanding of value proposition, strategy, and processes matter perhaps more than ever.
The company notes that evals are difficult to implement for the same reason building great products is difficult—they require rigour, vision, and taste. Done well, they become unique differentiators in competitive markets.
Looking Forward
OpenAI plans to share emerging best practices and frameworks as they develop. The company encourages organisations to experiment with evals and discover processes that work for their needs.
For those building on OpenAI’s API, platform documentation provides implementation guidance. The company invites industry experts to contribute to GDPVal, its latest benchmark measuring AI model performance on real-world tasks.
The fundamental message: don’t hope for “great” AI performance. Specify it, measure it, and improve toward it.
Source: OpenAI