How to Perform Comprehensive Large Scale LLM Validation

Validating and evaluating Large Language Model outputs is critical for building robust, large-scale applications. Even small error rates become significant when processing millions of requests, necessitating a strategy to catch and handle failures. Validation involves real-time checks on individual responses, which can be performed with simple code logic or by using a secondary, smaller LLM as a validator. For broader performance assessment, evaluation techniques like using an "LLM as a judge" to score outputs or collecting direct user feedback provide valuable quantitative metrics to guide improvements.