RAG Chunking: Why Splitting Documents the Right Way Makes AI Smarter

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Chunking is the process of breaking large documents into smaller, meaningful pieces before feeding them into a Retrieval-Augmented Generation (RAG) system. Rather than passing an entire document to an AI, chunking allows the retrieval system to surface only the most relevant section in response to a user query. The strategy behind chunking matters significantly — chunks that are too large introduce noise, while chunks that are too small can fragment meaning and lose context. Because chunking directly shapes the embeddings stored in a vector database, it ultimately determines what information an LLM receives when generating an answer. Choosing the right chunk size and overlap is therefore a core retrieval design decision, not just a text-splitting technicality.

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