RAG Evaluation: Measuring Retrieval, Grounding & Drift

Description

Retrieval-Augmented Generation systems don’t fail in one place — they fail across a chain. The retriever can return the wrong chunks. The model can hallucinate even when given the right context. And over time, the system can silently drift away from its original performance. In this video, I walk through a production-style evaluation framework for RAG systems that measures reliability across three critical stages: • Retrieval quality (Recall@K, MRR, Precision@K) • Grounding quality (Faithfulness, Hallucination Rate, Coverage) • System stability over time (Drift detection) Using a live Python demo powered by Groq, we trace a single query end-to-end and compute each metric step by step so you can see exactly how RAG reliability is measured in practice. This is not a beginner tutorial. This is how production teams think about trust, observability, and correctness in real-world RAG systems. If you're building RAG pipelines for enterprise use cases, this video will help you move from “it works” to “it’s measurable and reliable.” #RAG #GenerativeAI #LLM #AIEngineering #MLOps #VectorSearch #LangChain #LlamaIndex #AIObservability #MachineLearning #AIArchitecture #Groq