This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. RAG Chunking Strategies: Semantic Chunking, Overlapping, Recursive Splitting Introduction Document chunking is the foundation of any RAG system. How you split documents into chunks directly determines retrieval quality: chunks that are too small lose context, chunks that are too large dilute relevance, and naive splits break semantic units mid-thought. This article covers the major chunking strategies and when to use each. Naive Fixed-Size Chunking The simplest approach splits text every N characters or tokens: def fixed_size_chunks(text: str, chunk_size: int = 512, overlap: int = 64) -> list[str]: chunks = [] start = 0 while start < len(text): end = start + chunk_size chunk = text[start:end] chunks.append(chunk) start = end - overlap return chunks Enter fullscreen mode Exit fullscreen mode Fixed-size chunking is fast and predictable.…