How to Performance Test RAG Apps for Speed and Answer Quality in CI/CD

A technical guide published on DEV Community outlines a dual-gate approach to performance testing Retrieval-Augmented Generation (RAG) applications. Traditional load testing tools like k6 can measure response times but fail to detect hallucinations, where a model returns fast yet factually incorrect answers. The guide recommends pairing k6 for latency metrics — including Time to First Token and inter-token latency — with DeepEval, which uses an LLM-as-judge method to score faithfulness and answer relevancy. Both testing layers are integrated into a GitHub Actions CI/CD pipeline, enabling automatic regression detection on every pull request before code reaches production. The approach addresses a core weakness in conventional API testing: a RAG system can appear performant while silently generating unreliable, fabricated responses.