Honest eval methodology

Tue, 12 May 2026 00:00:00 +0000

Part of my AI Security Engineering series, a local email-to-calendar agent I build, then attack, then harden. If you have not set up the lab yet, start with Set up your lab. The lessons are on GitHub, and the agent itself lives at t0ul/emaildrop.

When the ruler is a model

The flywheel runs on measurement, and some of the things worth measuring do not have a tidy numeric answer. Was this summary faithful? Was this reply helpful? The usual move is to ask a model: give a stronger LLM the rubric and the output and have it return a score, model-as-judge, often sampled k times so you can talk about pass@k, the chance the agent gets it right in k tries. It is a genuinely useful technique, and it has a sharp edge that the rest of this book should make you flinch at: the judge reads the output, and the output came from a system that reads untrusted email.

Evaluation on t0ul

Honest eval methodology

When the ruler is a model