AI Systems · core
Chunking
Splitting documents into retrievable units that preserve meaning.
Mental model
A chunk is the atomic unit of retrieval — too big and embeddings blur, too small and context is lost. Good chunking respects structure (headings, functions, paragraphs) and adds overlap so answers are not cut in half.
How to study Chunking
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Pinecone — Chunking strategies to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Chunking with Context Packing, Embeddings. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Build a structure-aware chunker and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Where it matters
Every RAG ingestion pipeline.
Common mistakes
- Fixed character splits that cut sentences mid-thought
- Zero overlap, so boundary-spanning answers are lost
- One chunk strategy for every document type
Learn from primary sources
- Pinecone — Chunking strategies (article)
Practice and explain it back
Build a structure-aware chunker
Write a chunker that splits on document structure (headings/paragraphs) with a target size and overlap, instead of fixed character cuts.
Expected evidence: Chunks that respect structure and overlap at boundaries.
Open the interactive drill →Review prompts
- What is the tradeoff in choosing chunk size?
Build evidence
RAG pipeline v0
An end-to-end retrieve → pack → generate pipeline grounded in a document corpus.
- Ingest documents with a chunking strategy
- Retrieve relevant chunks for a query
- Generate an answer grounded in retrieved context
- Have at least 10 eval questions