To me, a successful eval meets the following criteria. Say, we currently have system A, and we might tweak it to get a system B:
- If A works significantly better than B according to a skilled human judge, the eval should give A a significantly higher score than B.
- If A and B have similar performance, their eval scores should be similar.
Whenever a pair of systems A and B contradicts these criteria, that is a sign the eval is in “error” and we should tweak it to make it rank A and B correctly.
Recent articles
- LLM 0.27, the annotated release notes: GPT-5 and improved tool calling - 11th August 2025
- Qwen3-4B-Thinking: "This is art - pelicans don't ride bikes!" - 10th August 2025
- My Lethal Trifecta talk at the Bay Area AI Security Meetup - 9th August 2025