Automating AI Evals: How Braintrust Uses Codex to Turn Expectations into Test Code

The Challenge of Automating AI Model Evaluation

In the development of AI products, designing "evals" to verify that a model is behaving as expected and implementing those as test code is a notoriously tedious process. Braintrust has addressed this bottleneck by leveraging OpenAI's Codex to automatically convert natural language descriptions of "expected behavior" into functional evaluation code. This approach dramatically accelerates the quality improvement cycle for AI systems.

The Codex-Driven Evaluation Workflow

The core of Braintrust's workflow lies in its ability to take a natural language requirement—such as "the model should respond in this specific way"—and use Codex (OpenAI's model specialized for code generation) to translate that expectation into verifiable evaluation code, such as assertions. This isn't mere code completion; it is a dedicated pipeline that interprets requirements for AI behavior and translates them into concrete test implementations.

By allowing Codex to generate the evaluation logic rather than requiring engineers to manually write vast libraries of test cases, Braintrust has significantly reduced the time needed to build evaluation sets. This enables a high-velocity loop of "behavior definition $\rightarrow$ evaluation code implementation $\rightarrow$ verification," allowing teams to iterate quickly on AI safety and accuracy.

Key Takeaways for AI Engineers

Braintrust's approach offers several practical insights for developers building AI-driven products:

1. Natural Language Eval Definitions: Instead of starting with rigid specifications, feeding expected behaviors directly into an LLM to generate boilerplate evaluation code can drastically reduce lead time for test implementation.
2. The Value of Specialized Models: Integrating code-specific models like Codex into the pipeline—rather than relying solely on general-purpose chat models—ensures the generated code is precise and compatible with the existing testing framework.
3. Building a Unified Pipeline: The key is not just generating isolated snippets of code, but designing a systematized flow that automates everything from the definition of behavior to the generation of code and the execution of the tests.

By treating AI not just as a coding assistant, but as an "evaluation generator," engineers can shift their focus away from repetitive testing tasks and toward more fundamental model improvements and architectural optimizations.

Conclusion

Braintrust's implementation demonstrates that LLMs can serve as a powerful bridge, converting "unstructured expectations" regarding model behavior into "structured evaluation metrics" in the form of test code. For any team looking to rapidly increase the reliability and robustness of their AI products, this is a highly effective and scalable approach.