STOP Ignoring LLMEvals! Build a Robust RAG EvaluationPipeline (LLM as a Judge & Ragas DeepDive)

Description

Are 60–80% of LLM developers ignoring pipeline evaluation? It’s time to stop. If you're building RAG (Retrieval-Augmented Generation) systems or LLM applications, proper evaluation is not optional. This video breaks down everything you need to build, trace, evaluate, and scale a production-ready RAG evaluation pipeline. In this deep dive, you will learn the tools, architectures, and metrics needed to ensure your LLM systems are accurate, reliable, and production-grade. What You Will Learn: 1. Why Domain Expertise Matters How domain experts help identify correct context, user intent, and dataset validation. 2. Creating Diverse Evaluation Datasets Designing datasets across personas like student, expert, technical, non-technical, and across tasks like Q&A, summarization, translation, and different contexts such as in-context, out-of-context, multi-context. 3. Advanced Dataset Generation with Ragas Using Ragas for synthetic data generation, leveraging knowledge graphs, transforms, keyword extraction, and headline generation for richer evaluation cases. 4. Three Core Evaluation Methods LLM as a Judge: Relevance scoring, groundedness, and binary scoring. Metrics-Based Evaluation: Precision, recall, faithfulness, and correctness. Rubrics-Based Evaluation: For structured scoring like resumes, reports, and domain-specific evaluations. 5. Tracing and Observability with OPIc (Comet ML) How OPIc helps track queries, context, answers, latency, token usage, cost, and dataset versioning. 6. Architecture Using LangGraph Building a robust RAG pipeline with LangGraph state management, Qdrant as vector database, and OpenAI for embeddings and LLMs. Who Should Watch This Video: LLM engineers, RAG system developers, AI researchers, and anyone building evaluation-first LLM applications. Next Video: Automating evaluation pipelines using real-time user input. Keywords / Tags: RAG Evaluation, LLM as a Judge, Ragas, LLM Evals, RAG Pipeline, OPIc, Comet ML, LangGraph, Dataset Generation, Knowledge Graphs, Faithfulness, Groundedness, Context Relevance, Answer Relevance, Context Precision, Qdrant, Vector Databases, Streamlit RAG App, LLM Observability, AI Evaluation, Keyword Extraction, Recursive Character Text Splitter, Evaluation-Driven Development Hashtags: #RAGEvaluation #LLMEvaluation #LLMasAJudge #Ragas #LangGraph #Qdrant #CometML #OPIc #RAGPipeline #LLMEvals #AIEngineering #NLPEngineering #AIEvaluation #RAGSystems #GenAI