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
- Olmo 3 is a fully open LLM - 22nd November 2025
- Nano Banana Pro aka gemini-3-pro-image-preview is the best available image generation model - 20th November 2025
- How I automate my Substack newsletter with content from my blog - 19th November 2025