Evaluation Guidelines and Annotation Design
~35 min read
Writing evaluation guidelines that produce consistent, high-quality labels at scale.
Evaluation Guidelines and Annotation Design is a Pro topic
Sign in, then upgrade to Pro or Power to unlock this topic and the full AI Engineering curriculum.
Key points
- •Anchor examples per rating level are the most important part of annotation guidelines — abstract descriptions alone produce low IAA
- •Target Cohen's kappa ≥ 0.7 before scaling annotation; refine guidelines if below this
- •Test sets need three source types: curated (known failures), sampled (real distribution), adversarial (edge cases)