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Choosing RAG Chunk Boundaries and Overlap to Preserve Section Context

This article explains how to choose effective chunk boundaries and overlap in retrieval-augmented generation (RAG) systems, comparing fixed-size and document-aware strategies. Using a hypothetical support note example, it demonstrates the risks of splitting across semantic units and shows how metadata and overlap can preserve context. The guidance is based on Microsoft Azure documentation for chunking in vector search and RAG workflows.

RAG / chunking / Azure AI Search / information retrieval / document processing

Understanding Semantic Search: How it Works and Why Similar Text Might Not Answer Your Question

Explore the principles of semantic search, its reliance on embeddings, and the reasons why text that appears similar might not provide the desired answer. Learn about vector search, hybrid search, and their applications in information retrieval.

semantic search / vector search / embeddings / AI / information retrieval / natural language processing / hybrid search / Azure AI Search