Preface

Designing and managing controlled, reliable, multimodal generative AI pipelines is complex. RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that will balance performance and costs.

From foundational concepts to complex implementations, this book offers a detailed exploration of how RAG can control and enhance AI systems by tracing each output to its source document. RAG’s traceable process allows human feedback for continual improvements, minimizing inaccuracies, hallucinations, and bias. This AI book shows you how to build a RAG framework from scratch, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques in optimizing performance and costs, improving model accuracy by integrating human feedback, balancing costs with when to fine-tune, and improving accuracy and retrieval speed by utilizing embedded-indexed knowledge graphs.

Experience a blend of theory and practice using frameworks like LlamaIndex, Pinecone, and Deep Lake and generative AI platforms such as OpenAI and Hugging Face.

By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.