Stop Guessing: How to Actually Evaluate Your RAG Pipeline

Description

Building a baseline RAG (Retrieval-Augmented Generation) pipeline is easy, but making it production-ready and hallucination-free is hard. How do you know if your LLM is fabricating answers, or if your vector database is just failing to retrieve the right documents? In this video, we break down the complex world of RAG evaluation. We will decipher the two core components of any RAG system: 🔍 Retrieval Metrics (Process Accuracy): How to measure if you are finding the right data using metrics like Precision@K, Recall@K 🧠 Generation Metrics (System Fidelity): How to measure if your LLM is using the data correctly by tracking Faithfulness, Answer Relevance, and Context Utilization Key Takeaway: A good RAG system requires both good retrieval AND grounded generation. Stop operating on "vibes" and start using data-driven metrics to optimize your chunking strategies, embeddings, and prompts! Don't forget to LIKE and SUBSCRIBE for more deep dives into AI Engineering, LLMs, and Retrieval-Augmented Generation! #RAG #GenerativeAI #LLM #MachineLearning #AIEngineering #RAGAS #LangChain #LlamaIndex #Python