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Retrieval Augmented Generation (RAG) › Semantic Search in RAG Pipelines

How does Semantic Search in RAG Pipelines work

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The system converts a user's query into an embedding and compares it against stored document embeddings, retrieving the pieces of content that are most similar in meaning.
Real-world example Semantic search in a RAG pipeline can find a relevant refund policy document even if the user's question uses completely different wording than the document.

Common follow-ups: What is Retrieval Augmented Generation (RAG), How is Semantic Search in RAG Pipelines evaluated in practice, What tools are commonly used for Semantic Search in RAG Pipelines

Retrieval Augmented Generation (RAG) topics: Introduction to Retrieval Augmented Generation Vector Databases for RAG Embedding Models for Retrieval
All 7 Semantic Search in RAG Pipelines questions