Evaluating RAG Systems: Measuring Retrieval Quality, Grounding, and Hallucinations

Part 3 of a series on building reliable AI systems In Part 1, we explored why testing AI systems is different. In Part 2, we built evaluation pipelines. Now let’s focus on one of the most widely used (and misunderstood) patterns: Retrieval-Augmented Generation (RAG). RAG is often seen as a solution to hallucinations. In reality, it just shifts the problem. The Core Problem with RAG A typical RAG pipeline looks like this: User Query ↓ Retriever → Context ↓ LLM → Response When something goes wrong