> ## Documentation Index
> Fetch the complete documentation index at: https://aitutorial.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview & Learning Objectives

> Overview and learning objectives for RAG

## 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.

## 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

## 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

## 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