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Retrieval-Augmented Generation (RAG) depends on one invisible step — chunking. The way we split information decides what an AI system remembers, how it connects ideas, and what it can prove when asked to explain itself. In this video, we explore chunking strategies — from fixed-size and recursive to semantic, hybrid, and LLM-guided — and how each method carries its own trade-off between context, precision, and cost. We’ll also see how poor chunking can break retrieval, why overlap matters, and how smarter chunking can make retrieval feel less like search, and more like understanding. ✦ Topics covered: ▸ Why chunking quality decides RAG performance ▸ Fixed-size, recursive, semantic, document-aware, and adaptive chunking ▸ How overlap, coverage, and redundancy impact retrieval ▸ Testing chunking quality with coherence and context-preservation metrics ▸ Philosophical reflection — if chunking shapes memory, what does that say about human understanding? — 🎙️ This is Logical Lenses. Where AI meets ideas. 🧠 Subscribe for deep, thoughtful explainers on AI, retrieval, and machine learning systems. #AI #RAG #Chunking #Retrieval #MachineLearning #VectorDatabases #LogicalLenses