Core

Chunking

Chunking divides documents into the passages you embed and retrieve, and it ranks among the highest-leverage knobs in RAG. Make chunks too large and embeddings get diluted so retrieval turns imprecise; make them too small and chunks lose the context needed to answer. Past fixed-size splitting, structure-aware and semantic chunking hold coherent units together, while parent-child (small-to-big) retrieval matches on small chunks yet returns larger context. AI, ML, and GenAI interviews test it because weak chunking quietly caps retrieval quality.

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RELATED CONCEPTS

The RAG Pipeline Embeddings Vector Search and ANN Indexes Hierarchical Retrieval (RAPTOR and Small-to-Big)

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignCompare chunking strategies (fixed-size, recursive, semantic, parent-child). How do you pick?→RAG & Agent System DesignWhat is semantic chunking, and how does it compare to fixed-size chunking?→RAG & Agent System DesignWhat is parent-child (small-to-big) retrieval, and why does it improve RAG?→Coding & DSAImplement chunking strategies from scratch: fixed-size, recursive, semantic, and parent-child.→RAG & Agent System DesignWhat is HyDE (Hypothetical Document Embeddings), and why does it improve retrieval?→RAG & Agent System DesignWhat is contextual retrieval, and how does it fix the lost-context problem in chunking?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Cognition

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