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RAG

Connect LLMs to external knowledge using chunking, vector databases, semantic search, and grounded generation.

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RAG Foundations

(3 topics)

Introduction to RAG (Retrieval-Augmented Generation)

An ultra-clear, beginner-friendly guide to Retrieval-Augmented Generation (RAG)—what it means, the closed-book vs. open-book exam analogy, RAG vs. Fine-Tuning, and when to use it.

Why Do We Need RAG?

Understand the exact technical and economic reasons why standalone LLMs fail in production—from parametric memory cutoffs and hallucination mechanics to the 'Lost in the Middle' problem and long-context token costs.

RAG Architecture & Workflow

Master the complete end-to-end blueprint of a RAG system—understanding the crucial separation between Offline Indexing (Load, Chunk, Embed, Store) and Online Querying & Generation.

Data Ingestion

(1 topic)

Documents & Data Preparation: Loading, Cleaning & Metadata for RAG

Learn where a RAG system's knowledge actually comes from—how to parse PDFs, DOCX, HTML, Markdown, CSV, and JSON, clean noisy text, and attach rich Metadata for precision retrieval.