TL;DR Separating the generator from the evaluator improves quality and reduces premature...
TL;DR
Separating the generator from the evaluator improves quality and reduces premature self-validation.
The loop works best when feedback is explicit and based on clear rubrics, especially for subjective or complex tasks.
It is useful when the task has high value; for simple or easily testable tasks, it can become overengineering.
How to Separate Production and Evaluation in Tasks Without Ground Truth
The Core Problem You shipped an AI agent. It works in demos. Then it runs 10,000 times in...
In this article, you will learn how to evaluate AI agents rigorously by examining their full execution process rather than only…
Author(s): Nitingummidela Originally published on Towards AI. Building a Critic-Agent Loop: Scores, Refinement, and GuardrailsA…
tl;dr — Agents are good at small fixes and terrible at "make this algorithm better" because every...
When I started learning about AI agent evaluation, I thought evals were mostly about checking the...
How to measure AI prompt quality, build an automated evaluation harness, and iterate to a quality plateau. 5 versions, 150…
