RAG & Vector Embeddings Architecture

Understand how RAG connects language models to private documents and databases to prevent hallucinations.

The 4-Step RAG Pipeline

1Chunking: Dividing large PDFs and documentation into 500-token chunks with overlap.

2Embedding: Converting text into high-dimensional vector arrays using embedding models.

3Retrieval: Searching a vector database (Pinecone, pgvector) using cosine similarity for the query.

4Generation: Injecting the top-k retrieved excerpts into the prompt as verified ground truth.

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What is the primary purpose of Retrieval-Augmented Generation (RAG)?