1 How RAG research prevents disasters

Retrieval-Augmented Generation (RAG) is an architectural pattern that enhances large language models (LLMs) by connecting them to external knowledge sources in real time. Unlike search engines that return documents for humans to read, a RAG system performs the synthesis itself. Unlike standalone language models that rely solely on training data, it retrieves relevant information to inform AI-generated responses and generates answers grounded in that evidence. This distinction is critical: search tools find sources; chatbots generate answers based on broad training; RAG systems use sources to construct reliable, verifiable answers.

1.1 How this book teaches RAG

1.2 How do RAG systems work?

1.3 How RAG research addresses failure points

1.3.1 The knowledge boundary problem

1.3.2 The hallucination challenge

1.3.3 The private knowledge access problem

1.3.4 RAG is an enduring architectural pattern

1.3.5 Why RAG justifies infrastructure investment

1.4 Failure points within RAG systems

1.4.1 RAG failure examples

1.4.2 Research-backed solutions

1.5 Naive, advanced, modular, and agentic RAG

1.5.1 Naive RAG: Establishing the foundation

1.5.2 Advanced RAG: Optimizing the components

1.5.3 Modular RAG: Adaptive and composable systems

1.5.4 Agentic RAG: Autonomous information-seeking systems

1.5.5 RAG evolution

1.6 Summary