Building An Evaluator

This workshop reframes the typical LLM agent as an evaluator-optimizer loop: one call generates a response while another evaluates it, then feedback guides the next step. It emphasizes moving logic from a pure agent to an evaluative loop and optimizes around it. It also covers displaying sources in the frontend as card-like items (favicon, title, snippet) to mirror DeepResearch patterns.

Source

Matt Pocock — AI Hero

Ingested

APR 30, 2026 · 04:08

Editorial score

3.0 / 5