Why it matters

Basic RAG is foundation. Understanding shapes advanced RAG.

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The architecture

Ingest: chunk + embed + store.

Query: embed + retrieve + generate.

Basic RAG flowChunk + embeddocs to vectorsVector storePinecone / pgvectorRetrieve top-ksimilarity searchFoundation for all RAG variants; understand before advanced
Basic RAG.
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How it works end to end

Chunking: 200-500 tokens typical.

Embeddings: OpenAI, Cohere, BGE.

Store: pgvector, Pinecone, Weaviate.

Retrieve top-k = 5-10.