intermediate• 20 min read• Published 6/15/2026
Introduction to RAG (Retrieval-Augmented Generation)
Learn how to build a scalable RAG pipeline for an enterprise knowledge base.
ragvector-dbllm
This is a brand new topic! AI Engineering is becoming a critical skill for Data Engineers. We will explore how to build scalable AI pipelines.
What is RAG?
Retrieval-Augmented Generation (RAG) is a technique that grounds Large Language Models (LLMs) with external knowledge.
Instead of relying solely on the LLM's internal training data, a RAG system first retrieves relevant documents from a Vector Database and then passes those documents to the LLM alongside the user's prompt.
The Standard RAG Pipeline
Architecture: Design a standard RAG pipeline with document ingestion, chunking, embedding, vector database storage, and a retrieval system answering user queries.
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- Ingestion: Documents are loaded, cleaned, and split into smaller chunks.
- Embedding: An embedding model (like OpenAI's
text-embedding-3-small) converts chunks into dense vector representations. - Storage: Vectors are stored in a Vector Database (e.g., Pinecone, Milvus, or pgvector).
- Retrieval: When a user asks a question, the query is embedded, and the database retrieves the top-K most similar document chunks.
- Generation: The retrieved chunks are injected into the LLM's context window to generate a grounded answer.
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