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Anonymousasked anonymously•
What chunking and overlap strategies produce the lowest latency in Retrieval-Augmented Generation (RAG) pipelines?
Static chunking patterns based on rigid character counts often split critical context phrases right down the middle, damaging the mathematical accuracy of your vector embeddings. For high-speed production RAG, use a dynamic semantic chunking strategy driven by document layout rules (such as Markdown headers or structural JSON trees). Maintain parent-child document relationship schemas where the vector database evaluates highly granular 200-token child chunks for semantic search, but passes the larger 1000-token parent context block to the LLM generation step. This preserves core contextual accuracy without overloading the model's primary attention window.