Chunking

Chunking is the process of splitting web pages and documents into smaller, self-contained segments that AI platforms can individually process, embed, and retrieve. Rather than treating an entire page as one unit, AI platforms break content into chunks of a few hundred to a few thousand tokens each, then evaluate each chunk independently for relevance to a query.

How content chunks determines what AI platforms can extract and cite. If content is structured with clear headings, self-contained sections, and explicit topic sentences, it chunks cleanly into meaningful units that accurately represent specific topics. If content is a wall of text with no structure, it chunks poorly, creating segments that lack context and fail to match specific queries. Research from LangChain found that optimal chunk sizes for retrieval range between 256 and 1,024 tokens, with 512 tokens producing the best balance of context and precision for most use cases (LangChain, 2024).

Optimizing for chunking means writing content where each section can stand alone and make sense without surrounding context. Use descriptive H2 and H3 headings, lead each section with a direct answer or topic sentence, and keep paragraphs focused on a single point. FAQ sections chunk particularly well because each question-answer pair is inherently self-contained. According to Pinecone's retrieval benchmarks, content with semantic section headers achieves 45% higher chunk relevance scores than content with generic or absent headings (Pinecone, 2024).