RAG Systems — Implementing Retrieval-Augmented Generation at Scale

Build ChatGPT... but for your own data. Learn how to ingest documents, retrieve relevant context, and generate grounded answers from your content.

Retrieval-Augmented Generation (RAG) is how modern LLMs "know" your data. This playbook shows you how to ingest docs, semantically search them, and return grounded answers using an LLM. Perfect for teams building support bots, internal search tools, or AI assistants that need to be factual, verifiable, and secure.

📘 What You'll Learn

•Understand how Retrieval-Augmented Generation enhances LLMs with external knowledge.
•Learn to ingest, chunk, and search documents using LangChain or LlamaIndex.
•Deploy scalable RAG pipelines with vector stores, APIs, and security best practices.

Playbook Chapters

Follow this step-by-step guide to build your RAG system

Chapter 1: Understanding Retrieval-Augmented Generation (RAG) and Its Architecture

Learn how RAG connects your LLM with real, private, and up-to-date data — the right way.

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Chapter 2: Ingesting and Chunking Documents — Using LlamaIndex or LangChain

Process PDFs, markdown, and HTML into smart chunks ready for vector search and fast retrieval.

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Chapter 3: Building a RAG Pipeline — Vector Search + LLM Answering

Combine semantic search with LLM generation to build an app that answers using your data.

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Chapter 4: Deploying RAG Systems — APIs, Scaling, and Security Best Practices

Host your RAG pipeline with confidence — handle auth, load, cost, and privacy at scale.

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