Designing for Evaluations is a key to minimising errors in an AI systems. It helps improve the system, keeps it safe and makes sure it meets user needs.There are three key methods to improve ML systems.

1. LLM-Based Evaluations (LLM-as-a-Judge)

A separate language model acts as an automated judge. It can grade responses, explain its reasoning and assign labels like helpful/harmful or correct/incorrect.

2. Code-Based Evaluations

Automated tests check if the output is correct.These are especially useful for structured tasks.

3. Human Feedback (Implicit and Explicit)

Real-world alignment needs direct user feedback inside the product. Common methods include Reaction buttons (thumbs up or thumbs down), Comment boxes and Clarification prompts. Read more about it on Design to Capture User Feedback

Amazon Bedrock: LLM-as-a-Judge Example

Amazon Bedrock uses the LLM-as-a-Judge approach to evaluate AI model outputs.A separate trusted LLM, like Claude 3 or Amazon Titan, automatically reviews and rates responses based on helpfulness, accuracy, relevance, and safety. For instance, two AI-generated replies to the same prompt are compared, and the judge model selects the better one.This automation reduces evaluation costs by up to 98% and speeds up model selection without relying on slow, expensive human reviews.