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Core
Hierarchical Retrieval (RAPTOR and Small-to-Big)
Hierarchical retrieval breaks the chunk-granularity dilemma: small chunks retrieve precisely but lack context, large chunks carry context but retrieve poorly. RAPTOR builds a tree by recursively clustering and summarizing chunks, so retrieval can land on a precise leaf or a higher-level summary. Small-to-big (parent-child) embeds small chunks for matching but returns the larger parent for context. Applied-AI interviews probe it because it is the standard production fix once naive fixed-size chunking starts missing answers.
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RELATED CONCEPTS
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 parent-child (small-to-big) retrieval, and why does it improve RAG?→RAG & Agent System DesignHow do you choose chunk size and decide between dense, sparse (BM25), and hybrid retrieval?→RAG & Agent System DesignYour RAG system struggles with PDFs containing tables and complex layouts. How do you fix parsing?→RAG & Agent System DesignWhat is contextual retrieval, and how does it fix the lost-context problem in chunking?→RAG & Agent System DesignWhat is semantic chunking, and how does it compare to fixed-size chunking?→
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