The Digital Press

This article shares expert insights on best practices for evaluating AI systems, especially large language models and retrieval-augmented generation (RAG) applications. It emphasizes practical strategies like error analysis, custom tooling, and thoughtful metric design to improve AI evaluation workflows.

Why it matters: Effective AI evaluation is crucial for building reliable, user-aligned models and avoiding pitfalls like misleading RAG assumptions or poor annotation practices.

The big picture: AI evals require ongoing error analysis, custom interfaces, and a mix of simple and complex evaluators tailored to application-specific failure modes.

Quick takeaway: Key recommendations include starting with binary labels, focusing on retrieval and generation separately in RAG, and appointing a single expert annotator for quality consistency.

Commenters say: Readers appreciate the depth and practical advice but note the complexity of building custom tools and stress the importance of continuous iteration over one-size-fits-all solutions.