RAG Chunking Strategy

Why It Matters

Poor chunking causes content to be retrieved out of context or not retrieved at all. If your best content is split across chunk boundaries, AI systems may miss the key claim or citation-worthy passage. Chunking-aware content architecture directly improves citation frequency.

How It Works

RAG systems split documents into chunks (typically 200–1000 tokens), encode each as a vector embedding, and store them in a vector database. At query time, semantically similar chunks are retrieved based on cosine similarity between query and chunk embeddings. Chunk boundaries and overlap settings determine retrieval granularity.

Use Cases

• Structuring long-form guides so each H2 section answers one complete question
• Writing FAQ sections with self-contained Q&A pairs that chunk naturally
• Creating product documentation with discrete feature descriptions
• Designing knowledge base articles with atomic, single-topic sections
• Using summary paragraphs at the top of sections to improve chunk retrieval quality

Best Practices

• Write each section or subsection as a self-contained, independently meaningful passage
• Lead each section with the core claim or definition before elaborating
• Avoid splitting key information across unrelated sections
• Use consistent heading structure to create natural semantic boundaries
• Keep critical facts within 300–500 word chunks for optimal retrieval
• Include context-setting sentences that work even when read out of full-document context