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Module Overview

You’ve probably noticed: LLMs confidently answer questions about data they’ve never seen — hallucinating facts, missing recent information, and unable to cite sources. Here’s why that happens: LLMs only know what they were trained on. Your company’s documents, yesterday’s data, and proprietary knowledge are invisible to them. In this module: You’ll build production RAG systems that connect LLMs to your data — with retrieval, chunking, evaluation, and reranking patterns that work at scale.

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Learning Objectives

By the end of this module, you will be able to:
  • ✅ Implement the core RAG pattern: retrieve, augment, generate
  • ✅ Choose between lexical, semantic, and hybrid search strategies
  • ✅ Design chunking strategies for different document types
  • ✅ Process unstructured data: PDFs, images, tables
  • ✅ Evaluate retrieval and generation quality independently
  • ✅ Apply reranking for precision optimization
  • ✅ Implement advanced patterns: GraphRAG, iterative RAG, hybrid data RAG

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Why This Matters

RAG is the workhorse of production AI systems:
  • It solves the knowledge problem: LLMs are frozen at training. RAG connects them to current, proprietary data
  • Cost-effective: Update your knowledge base without retraining — no fine-tuning costs
  • Grounded responses: Constraining generation to retrieved context reduces hallucinations
  • Auditable: Every answer links back to source documents
Case Study: Legal Document Analysis A law firm tried using GPT-4 directly to answer questions about case law:
  • Hallucinated legal precedents (dangerous)
  • Couldn’t access the firm’s proprietary case notes
  • No way to cite sources or verify claims
The RAG Solution:
  • Retrieved relevant cases and notes from their database
  • Constrained the LLM to only use retrieved context
  • Generated answers with citations to source documents
  • Result: 95% accuracy, full auditability, zero hallucinations on verified cases

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What You’ll Build

  • Basic RAG — keyword search + LLM generation over company documents
  • Semantic search — vector embeddings with provider-agnostic models (OpenAI, Gemini)
  • Hybrid search — BM25 + semantic with Reciprocal Rank Fusion
  • Cross-encoder reranking — two-stage retrieval for precision
  • PDF pipeline — digital + scanned PDF extraction, table parsing, image captioning
  • RAG evaluation — retrieval metrics (Hit Rate, MRR) and LLM-as-Judge for generation quality