How to Build Production-Grade LLM Evaluation Pipelines

A team shares how they replaced manual 'vibe checks' with automated evaluation for LLM-based systems, catching 92% of hallucinations before deployment. The pipeline uses domain-specific judges, CI/CD integration, and golden datasets to detect regressions. It aims to prevent incidents like a customer support bot that hallucinated policies for 500+ users.