Chunk size optimization balances context completeness with retrieval precision in RAG systems. Trade-offs: Small chunks (100-200 tokens): Precise retrieval, less noise, but may split context, multiple chunks needed, embedding overhead. Large chunks (1000+ tokens): Complete context, fewer chunks, but less precise retrieval, may include irrelevant content. Factors to consider: Document type (structured vs narrative), query patterns (specific vs broad), embedding model context limits, LLM context window. Empirical guidance: 256-512 tokens often optimal for general use, technical docs may prefer smaller (more precise), narratives may prefer larger (maintain flow). Dynamic chunking: Vary size based on content structure (section boundaries, paragraphs). Evaluation approach: Test multiple sizes on representative queries, measure retrieval recall and answer quality. Relationship with overlap: Overlap mitigates splitting issues for any chunk size. Semantic chunking: Use LLM/heuristics to chunk at semantic boundaries rather than fixed sizes. Best practice: Start with 400-500 tokens, 50-100 overlap, tune based on evaluation results.