When comparing two AI-generated Python functions, a software engineer asks: Does this actually work? What happens with empty input? Is the time complexity optimal? Are there SQL injection risks? Is the variable naming clear enough for a junior engineer to maintain? Would this pass a code review at a senior level? These are the questions that produce high-quality code preference data.
General-purpose RLHF annotators — who are excellent at evaluating prose quality — cannot reliably answer these questions. They tend to prefer code that looks clean superficially (well-spaced, with comments) over code that is correct and secure, and they cannot distinguish O(n) from O(n²) algorithms. Studies on code preference annotation with non-engineers show that annotators regularly reward incorrect solutions that run without syntax errors over correct but more complex solutions.
Our Code RLHF service uses experienced software engineers (5+ years, verified) as annotators. Each pair is evaluated on all five quality dimensions. Automated execution of the code against a test suite runs alongside human review — giving you both execution-verified correctness and human judgment on readability and style. Security linting (Bandit for Python, ESLint security rules for JS) runs automatically to flag vulnerabilities the human reviewer should investigate.
