Chunking Documents for Retrieval
Split documents into retrievable chunks that keep their meaning, then tune chunk size with real measurements.
A taste of a lesson
Retrieval finds the right policy document but returns a chunk without the actual answer. Why?
Often the answer and the words that match the question ended up in different chunks. The heading says Refund policy for annual plans, but the rule itself sits two paragraphs later in a chunk with no mention of refunds or annual plans. Prepend the document title and section heading to each chunk's text before embedding, so every piece carries its context. Parent and child chunks also help. Can you show me the chunk that was returned and the one holding the answer?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain why chunking matters for embedding and retrieval quality
- Apply fixed size, recursive, structure aware and semantic chunking
- Balance chunk size and overlap for your documents and questions
- Add context with section headers and parent and child chunks
- Compare chunking options with recall measured on real questions
Lesson plan
- 1 Why chunk at all Understand how chunking affects what retrieval can find. Start
- 2 Chunking strategies Compare fixed size, recursive, structure aware and semantic splitting. Start
- 3 Size and overlap trade offs Choose chunk size and overlap based on your questions. Start
- 4 Putting context back Help chunks carry the context they lost when split. Start
- 5 Cleaning and metadata Remove noise and attach useful metadata to each chunk. Start
- 6 Measuring chunking choices Pick a chunking setup using recall on a real question set. Start
Try asking
About this tutor
For developers building retrieval or RAG over manuals, policies, wikis, transcripts or code who suspect their chunking is hurting answers. You learn why documents are chunked at all, the main strategies (fixed size with overlap, splitting on structure, semantic splits), the trade off between small precise chunks and large contextual ones, and techniques that add context back: headings prepended to chunks, parent and child chunks, and keeping tables and lists whole. Everything is tied to measurement: you compare chunking choices on a small retrieval test set instead of copying someone else's chunk size.
Reviews
4.5
2 ratingsSample
- Vikram J.Sample
Looking at real chunks and asking whether a person could answer from them was the best habit I picked up. Table handling section was brief but useful.
- Aleksandra W.Sample
Prepending section headings to chunks improved our recall noticeably, and we proved it with the question set from lesson six. No more guessing chunk sizes.
About the teacher
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9 tutors 374 lessons taught Sample
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split,...
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