Mastering RAG Evaluation | Debug, Optimize, and Reduce Hallucinations

AIGrounded · Intermediate ·🔍 RAG & Vector Search ·2mo ago

About this lesson

Is your RAG (Retrieval-Augmented Generation) system giving wrong answers, but you aren’t sure why? Building an LLM application is just the first step—evaluating and observing its performance is what makes it production-ready .In this video, we dive deep into the two separate layers of RAG evaluation: Retrieval quality and Generation quality . We explore why RAG systems are non-deterministic and how to use tools like LangSmith to gain "factory-camera" visibility into every step of your pipeline . What You Will Learn: The Two Pillars of Evaluation: Why you must evaluate the retriever and the generator independently to find where the pipeline is breaking . Essential Retrieval Metrics: A breakdown of Precision@k, Recall@k, MRR (Mean Reciprocal Rank), and nDCG to measure how clean and relevant your search results are . Detecting Hallucinations: How to check for grounding by comparing generated answers against retrieved context and implementing "Answer Not Found" tests to stop the model from inventing information . The Power of Observability: Using LangSmith to trace the exact query sent, the metadata of retrieved chunks, and the specific prompt constructed to eliminate guesswork in debugging . Common Failure Modes: Identifying issues like bad chunking, embedding mismatches, context stuffing, and handling outdated vs. latest document conflicts . End-to-End Testing: How to create a test dataset with ground-truth answers to measure real-world performance . Whether you are preparing for a technical interview or optimizing a professional AI application, mastering these evaluation frameworks is essential for creating accurate, grounded, and consistent systems Hashtags #RAG #GenerativeAI #LangSmith #LLM #AIObservability #MachineLearning #VectorDatabase #AIQuality #PromptEngineering #DataScience

Original Description

Is your RAG (Retrieval-Augmented Generation) system giving wrong answers, but you aren’t sure why? Building an LLM application is just the first step—evaluating and observing its performance is what makes it production-ready .In this video, we dive deep into the two separate layers of RAG evaluation: Retrieval quality and Generation quality . We explore why RAG systems are non-deterministic and how to use tools like LangSmith to gain "factory-camera" visibility into every step of your pipeline . What You Will Learn: The Two Pillars of Evaluation: Why you must evaluate the retriever and the generator independently to find where the pipeline is breaking . Essential Retrieval Metrics: A breakdown of Precision@k, Recall@k, MRR (Mean Reciprocal Rank), and nDCG to measure how clean and relevant your search results are . Detecting Hallucinations: How to check for grounding by comparing generated answers against retrieved context and implementing "Answer Not Found" tests to stop the model from inventing information . The Power of Observability: Using LangSmith to trace the exact query sent, the metadata of retrieved chunks, and the specific prompt constructed to eliminate guesswork in debugging . Common Failure Modes: Identifying issues like bad chunking, embedding mismatches, context stuffing, and handling outdated vs. latest document conflicts . End-to-End Testing: How to create a test dataset with ground-truth answers to measure real-world performance . Whether you are preparing for a technical interview or optimizing a professional AI application, mastering these evaluation frameworks is essential for creating accurate, grounded, and consistent systems Hashtags #RAG #GenerativeAI #LangSmith #LLM #AIObservability #MachineLearning #VectorDatabase #AIQuality #PromptEngineering #DataScience
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