Chunking Strategy
Also: text splitting · document chunking
How you split source documents into retrievable units — it sets the ceiling on everything retrieval can return.
Why CCAR-P tests this
Chunking is the most common lever for fixing bad RAG results, so exams probe whether you understand the size/overlap/structure trade-offs.
Chunking divides documents into passages that get embedded and retrieved independently. Too large and each chunk carries diluted, multi-topic embeddings plus irrelevant filler that wastes context; too small and chunks lose the surrounding context needed to be meaningful. Overlap (repeating a few sentences between adjacent chunks) preserves continuity across boundaries.
Strategies range from fixed-size (by tokens/characters) to recursive/structural splitting that respects natural boundaries — headings, paragraphs, sentences, code blocks, or table rows. Semantic chunking groups sentences by embedding similarity so each chunk is one coherent idea. Structure-aware splitting usually beats naive fixed-size on real documents.
Attach metadata to each chunk (source, section, doc_id, timestamps, and crucially ACL/owner fields) so retrieval can filter and so downstream authorization can be enforced. Chunk boundaries also carry the identifiers used for per-document access control at retrieval time.
Exam trap
There is no universal chunk size — the right size depends on document structure and query type, so an answer asserting a fixed 'best' token count is a trap.
Commonly confused with
Related concepts
Independent, unofficial study material from Claude Cert Prep. Not affiliated with Anthropic.