Chunk size and overlap directly affect the precision and completeness of information retrieved in a RAG architecture. Microsoft identifies effective chunking as a critical part of successful RAG implementation. If chunks are too large, they can contain excessive unrelated information, reducing retrieval precision because the vector representation covers multiple concepts. Conversely, chunks that are too small can omit necessary surrounding context or separate related information across chunk boundaries, resulting in incomplete retrieval.

Overlap helps preserve semantic continuity between adjacent chunks. Microsoft recommends using overlap particularly with fixed-size chunking because relevant facts or concepts can span chunk boundaries. The optimal chunk size and overlap should therefore be evaluated against representative documents and queries rather than treated as fixed constants. Microsoft specifically notes that larger chunks can preserve contextual richness, while appropriately configured overlap prevents contextual information from being lost at segmentation boundaries.

Temperature controls generation variability , not document retrieval. Token limits constrain how much content a model processes but do not inherently make retrieval more relevant. Embedding strategy can also influence retrieval quality, but given the stated symptom of irrelevant or incomplete retrieved passages , tuning chunk size and overlap is the directly targeted remediation.

Study Guide Reference: Optimize generative AI systems and model performance — RAG optimization, document chunking, retrieval precision, contextual completeness, and grounding.

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