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.

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Видео RAG Chunking Explained: Strategies, Chunk Size, Overlap, and Retrieval канала Schovia