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Article Outline – Build a Production-Ready RAG Pipeline

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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