alt Hacker NewsYou've just described what the AI industry refers to as "evals", a collection of which forms a particular benchmark. I definitely encourage you to define your own evals, because the public benchmarks are often either saturated (largely solved and only going to see small improvements going forward) or seemingly not predictive of real world performance. I could also go further and speculate that they are in the training data, although credible benchmarks avoid this, I'm just not sure how successful they are.
Thanks for the comment - I’ve actually been working on something to generate evals from private repos, as I 100% agree that public benchmarks are either contaminated (as OpenAI found with SWE-bench Verified), not high quality (as OpenAI found with SWE-bench Pro), or in the case of “good” benchmarks (Cognition’s Frontier Code comes to mind), non-representative of a specific repo’s work.
Sharing some challenges / learnings from the journey so far:
* Tests alone are non-representative: an agent can pass tests but write the code in a way that’s subjectively worse than another test-passing result
* However, tests are still important and the best form of deterministic evaluation we have
* Creating executable environments to run the tests is challenging, especially when considering that this should be doable for arbitrary repos
* Selecting tasks that discriminate is an art - they need to be challenging, but not too challenging, represent the variety of work that’s done in the repo, and contain tests that failed prior to the change
* LLMs / agents are very powerful at judging code - but doing so in a way that is calibrated, consistent, and representative of codebase standards requires careful rubric creation and grading
* I’ve shifted towards using agents (from “static” LLM calls) to generate the rubrics, and to later grade the rubrics. This is powerful but even more non-deterministic