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Build a Production-Ready RAG Pipeline: A Step-by-Step Tutorial

1. Why RAG Matters — And What You’ll Build

• Understand the core problem: LLMs hallucinate on private or recent data — RAG (Retrieval-Augmented Generation) fixes that by grounding answers in your own documents.
• Walk away with a working pipeline that ingests PDFs, chunks them, embeds into a vector store, and answers questions via an LLM — all in under 100 lines of Python.
• We’ll use open-source tools: LangChain, ChromaDB, and

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  Editor’s Pick: A vector database for storing and retrieving immutable embeddings along with metadata.

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  Written byTheo Grant

  Theo Grant explores real-world AI applications, automation workflows, and hands-on tutorials at AI In Action Hub. Theo breaks down complex AI concepts into practical guides that help professionals and creators leverage AI in their daily work.