parent child
Applied AI interview questions tagged parent child, across every topic.
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Concepts behind "parent child"
The curriculum that explains the ideas these questions test.
Core
ChunkingChunking splits documents into the passages you embed and retrieve, and it is one of the highest-leverage knobs in RAG. Too large and embeddings are diluted so retrieval is imprecise; too small and chunks lose the context needed to answer. Beyond fixed-size splitting, structure-aware and semantic chunking keep coherent units intact, and parent-child (small-to-big) retrieval matches on small chunks but returns larger context. Applied-AI interviews probe it because poor chunking silently caps retrieval quality.🤖 Retrieval & AgentsSign in
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.🤖 Retrieval & AgentsSign in