OpenAI's Eval Framework Transforms AI Reliability

As over one million businesses leverage AI to drive efficiency and value creation, many struggle to achieve expected . OpenAI has developed evaluation frameworks that transform abstract objectives into consistent outcomes. The company uses these s internally to improve AI systems' ability to meet expectations, making both customer-facing and internal tools more reliable at scale. This approach decreases high-severity errors, protects against downside risk, and provides organizations with a measurable path to higher ROI.

At OpenAI, researchers use rigorous frontier models to measure performance across different domains. While these models help ship better products faster, they cannot reveal the nuances required to ensure models perform in specific settings. That's why teams created dozens of contextual evaluations designed to assess performance within internal workflows. Leaders should learn to create contextual evaluations tailored to their operating environment.

This primer provides a broad framework that OpenAI has seen work across many situations. The field continues to evolve, and frameworks will emerge to address different contexts and goals. For example, an excellent evaluation for a cutting-edge, AI-enabled consumer product might require different approaches than internal automation based on standard operating procedures. The framework serves as a collection of practices for both cases and provides a useful guide for building tailored solutions.

Organizations should start small with an empowered team that writes down the AI system's purpose in plain terms. Teams should mix individuals with technical expertise and domain knowledge. They should be able to state the most important outcomes to measure, outline end-to-end workflows, and identify decision points the AI system will encounter. At every workflow step, teams should define what success looks like and what to avoid.

The process involves creating mappings from dozens of example inputs to desired outputs. The resulting golden examples should serve as living, authoritative references representing the most skilled judgment of what great performance looks like. Teams should not try to solve everything at once—the process is iterative and messy. Early prototyping helps immensely, and reviewing 50-100 outputs from an early system version uncovers how it's failing.

Analysis in a taxonomy of errors and their frequencies to track system improvement. This process is not purely technical—it's cross-functional and centered on defining goals and desired processes. Technical teams should not be asked in isolation to judge what serves customers, products, sales, or HR. Consequently, domain experts, technical leads, and key stakeholders should share ownership.

The next step involves measurement. The goal is to reliably surface concrete examples of the system failing. This requires a dedicated test environment that closely mirrors real conditions, not just demo prompts in a playground. Evaluate performance against golden examples under the pressures the system will actually face. Rubrics bring concreteness to judging system performance, though they might over-emphasize superficial items at the expense of overall quality.

Some qualities are difficult or impossible to measure. In some cases, existing metrics are important; in others, organizations need to invent new metrics. Keep subject matter experts in the loop throughout, tightly aligning the process with core objectives. Test examples should be drawn from real situations whenever possible, including invented edge cases that would be rare but costly if mishandled.

Some evaluations can be scaled through LLM graders, where a model grades outputs the way an expert would. Yet organizations should still keep humans in the loop. Teams need to regularly audit LLM graders for accuracy and should directly review logs of system behavior. When evaluations are ready for launch, the process doesn't stop there. Organizations should continuously monitor the quality of the system's generated inputs.

Any signals from end-users—whether external or internal—are especially valuable and should be built into evaluations. The last step focuses on continuous improvement. Addressing problems uncovered by evaluations takes various forms: refining system prompts, adjusting access controls, updating the system itself, and so forth. Each iteration compounds upon the last: clearer criteria and expectations reveal subtle, stubborn issues to correct.

To support iteration, build a flywheel system. Log inputs, outputs, and outcomes; sample logs on a schedule; automatically route ambiguous or costly cases for review. Add these judgments to error analysis, then use them to update tools and models. Through this loop, clearer expectations lead to tighter performance, enabling tracking of additional outcomes. Deploying at scale yields large, differentiated, context-specific datasets that are hard to copy—a valuable asset organizations can leverage to build market advantages.

While evaluations create a systematic way to improve AI systems, failure modes still arise. In practice, models, data, and evaluations must be continuously maintained, expanded, and stress-tested. For external-facing deployments, evaluations do not replace A/B tests and experimentation. They complement experimentation by providing visibility into how changes impact performance.

Every major technology shift reshapes operational excellence and competitive advantage. Frameworks like OKRs and KPIs helped organizations orient themselves toward measuring what matters in the age of big data analytics. Evaluations are a natural extension for the age of AI. Working with probabilistic systems requires new kinds of measurement and deeper consideration of trade-offs.

Leaders must decide where precision is essential versus where more flexibility is needed, balancing velocity and reliability. Evaluations are difficult to implement for the same reason building great products is difficult: they require rigor, vision, and taste. When done well, they become unique differentiators. In a world where information is freely available and expertise is democratized, advantage hinges on how well organizations execute inside their specific context.

Robust evaluations create compounding advantages through institutional know-how and systems that improve over time. At their core, evaluations are about deep understanding of context and objectives. This means that for any use case, organizations are unlikely to achieve greatness without them. This highlights a key lesson of the AI era: AI management skills are management skills. Clear goals, direct feedback, prudent judgment, understanding value propositions, and strategy still matter—perhaps even more than ever.

As evaluation practices continue to emerge, OpenAI shares them with the community. In the meantime, the company encourages organizations to experiment with evaluations to discover their processes and needs. To get started, pick a problem that can be solved by a domain expert, set up a small team, and explore platform documentation if using APIs. Don't just hope for great AI—specify it, build toward it, and measure progress.

OpenAI would support building next-generation models and invites contributions to GDPVal, the latest benchmark for how AI models perform tasks. Industry experts interested in contributing to GDPVal can express interest, as can customers working with OpenAI who want to contribute to future GDPVal development.