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GraphRAG and Contextual Retrieval

Standard RAG retrieves chunks independently, which fails on questions whose answer is spread across documents or depends on a relationship nobody wrote down in one place. GraphRAG builds an entity graph so retrieval can traverse; contextual retrieval fixes a cheaper problem, which is that a chunk stripped of its document loses the meaning it needed.

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RELATED CONCEPTS

Retrieval-Augmented Generation (RAG)Chunking Strategies Reranking and Two-Stage Retrieval Hybrid Search (Lexical + Vector)The Ontology (Semantic Layer)

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringWhen an AI feature gives a wrong answer, retrieval is usually the reason13 min→

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks→RAG & Agent System DesignHow would you chunk documents for retrieval, and how do you know your chunking is good?→RAG & Agent System DesignKeyword search vs vector search: what does each actually buy you in a RAG system?→LLM & GenAI FundamentalsWhat are embeddings, and how do they make semantic search work?→LLM & GenAI FundamentalsWhy is the dot product the similarity score in attention and embeddings, and when should you normalize to cosine?→RAG & Agent System DesignHow would you chunk contracts, Slack threads, and PDFs full of tables: same pipeline or different?→