Hybrid Search
In one line
Hybrid search combines keyword (BM25) and vector retrieval to get the best of both — the default retrieval shape behind Perplexity-style answer engines and most production RAG.
Going deeper
Hybrid search runs traditional keyword retrieval (BM25 and friends) and vector retrieval at the same time, then merges the results. Keyword search shines on exact terms, proper nouns and numbers; vector search shines on meaning, synonyms and context. Together they cover each other's weak spots.
The marketing-side takeaway is that AI search is not purely lexical or purely semantic. Perplexity, ChatGPT Search and most production RAG stacks lean on hybrid retrieval, which means good GEO content needs both — exact names and clean keywords, plus the surrounding context that makes it findable by meaning.
How the two result sets are merged (RRF, weighted scoring, reranking) decides the quality. In practice hybrid search is almost always paired with a reranker downstream to clean up the final candidate list.
Related terms
RAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
LLMEmbedding
An embedding is a numeric vector representation of text or other data that preserves semantic meaning — the foundation of semantic search, vector databases and RAG.
LLMVector Database
A vector database stores embeddings and performs fast similarity search across them — the core infrastructure behind RAG and semantic search.
LLMReranker
A reranker re-scores the candidate documents that retrieval produced and reorders them — the final gate that decides which sources an AI actually cites.
GEO·AEOPerplexity
Perplexity is an answer engine that turns search results into a single cited answer, attaching a numbered source to every sentence — making it a common reference surface for measuring GEO performance.
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