The practice of breaking long-form content into semantically coherent, self-contained sections (or “chunks”) so that AI retrieval systems and embedding models can more precisely index, retrieve, and cite your material. Effective content chunking:

• Enhances Precision: Each chunk covers a single topic or question (e.g., an FAQ entry, sub-heading section), reducing noise in embedding vectors.

• Improves Retrieval: Smaller, focused chunks align more closely with user queries and RAG prompts, boosting the chances your content is surfaced and cited.

• Supports Schema & Linking: You can attach unique @id anchors, FAQ markup, or hasPart relationships to each chunk—feeding richer signals into knowledge graphs.

• Boosts Usability: Users (and AI) navigate directly to the most relevant section, decreasing bounce rates and increasing dwell time—both positive signals for LLM citation engines.