RAG Chunking Explained: Strategies, Chunk Size, Overlap, and Retrieval

Chunking strategy is one of the most important design decisions in Retrieval-Augmented Generation (RAG). Poor chunking can cause retrieval failures, incomplete context, and hallucinated answers before the LLM even generates a response. This video explains RAG chunking strategies by showing how document splitting affects retrieval quality, context preservation, and answer accuracy. You’ll learn why poorly structured chunks lead to weak retrieval, how fixed-size, semantic, and parent-child chunking work, and when each approach matters in production RAG systems. 🧠 In this video: -Fixed-size chunking and the chunk size tradeoff -Chunk overlap, boundary failures, and retrieval accuracy -Semantic chunking and meaning-preserving retrieval -Parent-child retrieval for balancing precision and context ⏱️Chapters 00:00 — Why RAG fails before the LLM answers 00:00:51 — What chunking is and why it matters 00:03:34 — Fixed-size chunking 00:04:23 — Chunk overlap 00:05:30 — Semantic chunking 00:07:09 — Structure-aware chunking 00:08:20 — Parent-child retrieval 00:09:39 — Designing chunks for different query types 00:10:42 — Key takeaways #MachineLearning #DataScience #RAG #Chunking #RetrievalAugmentedGeneration #LLM #VectorDatabase #ModelEvaluation 👩‍🏫 About the Presenter: Dr. Sindhu Ghanta delivers clear, practical, and mathematically intuitive explanations for complex machine learning algorithms. Her/Our style? No jargon. Just clear, useful explanations that help you learn fast and apply your skills immediately. 🔗 Learn More & Subscribe: Subscribe to @Schovia for weekly AI tutorials, simplified tech, and the latest trends. 🔗 Explore More at Schovia: https://schovia.com/ 🔔 Like, comment, and subscribe for new videos every Tuesday!