Research note: Model specifications and benchmark results are time-bound. Check the dated primary sources below before using them for a technical or purchasing decision.

Generative LLMs are probabilistic token samplers. Treating their raw code output as production-ready without automated verification harnesses guarantees catastrophic regressions.

Test-Driven AI Engineering requires a strict invariant: before an agent generates an implementation, it must write a failing test specification; after implementation, the test suite must execute inside a sandboxed runner with exit code 0.

Sources & Further Reading

  • • Automated Code Generation & Testing: Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code (Codex). OpenAI. arXiv:2107.03374.