Dev Tutorial: How to Automate RAG System Quality Evaluation Using Evals

A new developer tutorial introduces 'Evals', a method for automatically measuring the quality of Retrieval-Augmented Generation (RAG) system responses instead of relying on manual review. The approach involves building an evaluation dataset of questions, expected answer keywords, and reference documents to benchmark system performance. RAG quality is assessed across three dimensions: faithfulness (no hallucinations), answer relevancy, and context recall (retrieval accuracy). The tutorial provides sample Python code using pgvector, Google Gemini embeddings, and PostgreSQL to run automated scoring. Supporting scripts for dataset definition, RAG evaluation, agent evaluation, and report generation are included in the project structure.