فهرست مطالب

• A Simple Guide to Retrieval Augmented Generation
• brief contents
• contents
• preface
• acknowledgments
• about this book
• about the author
• about the cover illustration
• Part 1 Foundations
• Chapter 1. LLMs and the need for RAG
• 1.1 Curse of the LLMs and the idea of RAG
• 1.2 The novelty of RAG
• 1.3 Popular RAG use cases
• Chapter 2. RAG systems and their design
• 2.1 What does a RAG system look like?
• 2.2 Design of RAG systems
• 2.3 Indexing pipeline
• 2.4 Generation pipeline
• 2.5 Evaluation and monitoring
• 2.6 The RAGOps Stack
• 2.7 Caching, guardrails, security, and other layers
• Part 2 Creating RAG systems
• Chapter 3. Indexing pipeline: Creating a knowledge base for RAG
• 3.1 Data loading
• 3.2 Data splitting (chunking)
• 3.3 Data conversion (embeddings)
• 3.4 Storage (vector databases)
• Chapter 4. Generation pipeline: Generating contextual LLM responses
• 4.1 Generation pipeline overview
• 4.2 Retrieval
• 4.3 Augmentation
• 4.4 Generation
• Chapter 5. RAG evaluation: Accuracy, relevance, and faithfulness
• 5.1 Key aspects of RAG evaluation
• 5.2 Evaluation metrics
• 5.3 Frameworks
• 5.4 Benchmarks
• 5.5 Limitations and best practices
• Part 3 RAG in production
• Chapter 6. Progression of RAG systems: Naïve, advanced, and modular RAG
• 6.1 Limitations of naïve RAG
• 6.2 Advanced RAG techniques
• 6.3 Pre-retrieval techniques
• 6.4 Retrieval strategies
• 6.5 Post-retrieval techniques
• 6.6 Modular RAG
• Chapter 7. Evolving RAGOps stack
• 7.1 The evolving RAGOps stack
• 7.2 Production best practices
• Part 4 Additional considerations
• Chapter 8. Graph, multimodal, agentic, and other RAG variants
• 8.1 What are RAG variants, and why do we need them?
• 8.2 Multimodal RAG
• 8.3 Knowledge graph RAG
• 8.4 Agentic RAG
• 8.5 Other RAG variants
• Chapter 9. RAG development framework and further exploration
• 9.1 RAG development framework
• 9.2 Design stage: Layering the RAGOps stack
• 9.3 Ideas for further exploration
• Index