Retrieval-Augmented Generation

A Comprehensive Guide to Building Intelligent Search-Powered AI Systems

Introduction: The Context Revolution

1. What You Will Learn
2. Who This Book Is For
3. How to Use This Book

Chapter 1: The RAG Revolution – Why Context Is Everything

1. The Knowledge Gap in Large Language Models
2. From Fine-Tuning to Retrieval: A Paradigm Shift
3. What RAG Actually Is (and Is Not)
4. Why RAG Dominates Production AI Today
5. How This Book Is Structured

Chapter 2: Foundations – LLMs, Embeddings and the Information Retrieval Landscape

1. How Large Language Models Think (and Where They Fail)
2. The Anatomy of Text Embeddings
3. Information Retrieval: From TF-IDF to Dense Vectors
4. The Similarity Math Behind Retrieval
5. Bridging the Gap: Why These Three Must Work Together

Chapter 3: RAG Architecture – The Big Picture

1. The Three Phases of a RAG Pipeline
2. Indexing: Turning Raw Data into Searchable Knowledge
3. Retrieval: Finding What Matters
4. Generation: Synthesizing Answers from Context
5. Architectural Patterns: Naive, Modular and Advanced RAG

Chapter 4: Data Ingestion and Chunking Strategies

1. The Chunking Problem: Why It Matters More Than You Think
2. Fixed-Size and Recursive Text Splitting
3. Semantic and Structure-Aware Chunking
4. Metadata Extraction and Enrichment
5. Building a Production Document Ingestion Pipeline

Chapter 5: Embeddings – The Bridge Between Language and Search

1. How Embedding Models Work Under the Hood
2. Choosing the Right Embedding Model for Your Use Case
3. Open Source vs. Commercial Embeddings: A Practical Comparison
4. Fine-Tuning and Adapting Embedding Models
5. Evaluating Embedding Quality

Chapter 6: Vector Databases – Storing and Searching at Scale

1. What Makes a Vector Database Different
2. Indexing Algorithms: HNSW, IVF, PQ and Beyond
3. The Vector Database Landscape: Choosing Your Store
4. Schema Design for Vector Stores
5. Performance Tuning and Scaling

Chapter 7: Retrieval Techniques – Beyond Simple Similarity Search

1. The Limits of Pure Vector Search
2. Hybrid Search: Combining Dense and Sparse Retrieval
3. Query Transformation Techniques
4. Re-Ranking: The Secret Weapon of Good RAG
5. Multi-Vector and Graph-Based Retrieval

Chapter 8: Generation – Crafting Better Answers from Retrieved Context

1. Prompt Design for Context-Augmented Generation
2. Managing Context Window Constraints
3. Citation and Attribution in Generated Responses
4. Handling Conflicting or Insufficient Information
5. Multi-Turn Conversations and State Management

Chapter 9: Evaluation – Measuring What Matters

1. What to Measure in a RAG System
2. Retrieval Metrics: Recall, Precision and MRR
3. Generation Metrics: Faithfulness, Answer Relevance and More
4. Automated Evaluation Frameworks (RAGAS, DeepEval, Arize)
5. Building an Evaluation Pipeline for Continuous Improvement

Chapter 10: Optimization – Making RAG Fast and Cost-Effective

1. The Latency Budget: Where Time Goes in a RAG Request
2. Caching Strategies at Every Layer
3. Model Selection and Cost Optimization
4. Batch Processing and Throughput Scaling
5. Infrastructure Patterns for Production RAG

Chapter 11: Advanced RAG Patterns – Agentic, Multi-Modal and Beyond

1. Agentic RAG: Letting Models Decide How to Retrieve
2. Multi-Hop Reasoning with Iterative Retrieval
3. Self-RAG and Reflective Retrieval Patterns
4. Multi-Modal RAG: Beyond Text
5. The Frontier: What’s Next for RAG

Chapter 12: Security, Privacy and Governance

1. Data Privacy and PII Protection
2. Prompt Injection and Jailbreak Attacks
3. Access Control and Row-Level Security in RAG
4. Audit Trails and Compliance
5. Building a Security-First RAG Architecture

Chapter 13: Real-World Case Studies and Production Deployments

1. Enterprise Knowledge Bases and Internal Search
2. Customer Support and Helpdesk Automation
3. Legal Document Analysis and Contract Review
4. Healthcare and Medical Literature Retrieval
5. Lessons from Production: What Works and What Does Not

Chapter 14: Building a Complete RAG Application – A Hands-On Project

1. Project Setup and Architecture Decisions
2. Building the Document Ingestion Pipeline
3. Implementing Retrieval with Hybrid Search and Re-Ranking
4. The Generation Layer with Streaming and Citations
5. Deployment, Monitoring and Iteration

Conclusion: The Future of Context-Aware AI