LLMs are powerful, but they often struggle with self-correction. In this video, we implement the Evaluator-Optimizer design pattern to solve this. We will demonstrate how to separate the "generation" step from the "feedback" step, and most importantly, how to inject a Human-in-the-Loop (HITL) to ensure quality control before the loop continues.
We will look at how to architect a system where the AI generates a draft, a human (or automated test) evaluates it, and the AI optimizes based on that specific feedback.
In this video, we cover:
1. What is the Evaluator-Optimizer Pattern?
2. Architecture: Generator vs. Evaluator
3. Adding the Human-in-the-Loop (HITL) workflow
4. Code Walkthrough of the Design Pattern (from scratch)
Codebase: https://github.com/SauravP97/AI-Engineering-101/tree/main/evaluator-optimizer-agent
Agentic RAG paper: https://arxiv.org/pdf/2501.09136
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