Building Advanced RAG Pipelines and Indexes | Learn LLMs in Sinhala Ep. 24

Description

RAG architecture fundamentals – ingestion, indexing, retrieval, and generation - Key challenges of scaling RAG (hallucinations, irrelevant retrieval, stale data) - Advanced retrieval techniques (Hybrid Retrieval, Knowledge Graphs, Filtering, Reranking) - Smart indexing strategies (chunking, hierarchical iRetrieval-Augmented Generation (RAG) is one of the most powerful ways to ground Large Language Models (LLMs) with external, up-to-date knowledge. While basic RAG works for demos, production systems demand advanced strategies for accuracy, performance, and reliability. In this video, we cover: - ndexes, small2big chunking) - RAG optimization strategies - query enhancement, retriever improvement, generator tuning - Self-reflective RAG, corrective pipelines, and query routing with agents - Best practices for evaluating RAG (faithfulness, answer relevancy, contextual recall/precision) This session is ideal for AI researchers, data scientists, and developers who want to build production-ready RAG pipelines that deliver trustworthy and efficient answers.