Mar 20
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Description Building a RAG system is only half the battle. How do you prove it actually works? In this video, we dive into the essential framework for RAG Evaluation, ensuring your AI is both accurate in its search and grounded in its responses. We break down the evaluation process into two critical stages: the Retriever (was the right information found?) and the Generator (did the AI answer correctly based on that information?). You will learn about the industry-standard metrics used to benchmark custom LLM applications. What we cover in this lesson: The Two Evaluation Points: Assessing the Vector Database vs. the LLM Response. Retriever Metrics: Contextual Precision: Does the system rank relevant documents at the top? Contextual Recall: Does the retrieved info align with the ground truth? Contextual Relevancy: Is the retrieved context actually useful for the query? Generator Metrics: Answer Relevancy: Does the LLM answer the user's actual question? Faithfulness: Is the answer grounded in the retrieved context (preventing hallucinations)? Hallucination Checks: How to identify contradictory statements. LLM as a Judge: An introduction to G-Eval and using models to grade other models with Chain-of-Thought (CoT). By the end of this video, you’ll have a roadmap for testing your RAG pipeline against real-world metrics, moving from "vibe-based" testing to data-driven evaluation. #RAG #LLM #AIEvaluation #GenerativeAI #MachineLearning #DeepEval #ContextualPrecision #Faithfulness #NLP #DataScience