Core

Hybrid Search (Lexical + Vector)

Hybrid search runs a keyword retriever (BM25) and a dense vector retriever side by side, then merges their result lists, because each one misses cases the other catches. Vectors lose exact codes and rare jargon, BM25 loses paraphrase, and combining them with Reciprocal Rank Fusion usually beats either alone.

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RELATED CONCEPTS

Vector Databases Reranking and Two-Stage Retrieval Retrieval-Augmented Generation (RAG)Embeddings & Vector Representations

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringTrace the evidence before changing the prompt22 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 DesignDesign a hybrid retrieval stack: BM25, vectors, and a reranker. What does each stage rescue?→