← 🤖 Retrieval & Agents

Core

RAPTOR and Small-to-Big: Hierarchical Retrieval for RAG

The fix for the chunk-size tradeoff: RAPTOR clusters and summarizes into a tree, small-to-big embeds child chunks but returns the parent for context.

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TAUGHT IN THESE COURSES

This concept is a reference. These lessons put it in sequence, with what comes before and after it.

Applied AI Engineering·RetrievalFree14mChunking: the decision that quietly caps your qualityHow you split documents into passages sets a ceiling on how good your retrieval can ever be, and it is usually decided in five minutes by a default. This lesson covers the tension every strategy is trading against, and how to choose deliberately.

RELATED CONCEPTS

Chunking The RAG Pipeline GraphRAG and Knowledge-Graph Retrieval Reranking

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?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Cognition

NEXT IN RETRIEVAL & AGENTSQuery Transformation and Multi-Hop Retrieval→