Angielski
Generative AI has transformed industries, enabling the creation of human-like text, images, and code. However, traditional generative models often suffer from inaccuracies and hallucinations due to their reliance on pre-trained data. Retrieval-Augmented Generation (RAG) addresses this limitation by integrating retrieval mechanisms, enhancing the quality, accuracy, and relevance of generated content.
Beyond the foundational aspects, this book delves into the core mechanisms that make RAG more effective than traditional generative models. It covers advanced embedding techniques for efficient knowledge retrieval, vector database optimization, and fine-tuning transformer models to dynamically fetch and incorporate external knowledge into generated responses. Readers will also explore dense retrieval methods, indexing strategies, and real-time query optimization to enhance generative model performance.
Additionally, the book explores the synergy between RAG and Large Language Models (LLMs), discussing how hybrid architectures can be designed for improved accuracy, lower computational costs, and reduced model hallucinations. Through case studies and hands-on examples, readers will gain practical insights into deploying RAG-based AI systems at scale, optimizing inference speeds, and ensuring data relevance in diverse application domains. The final chapters will present emerging trends in retrieval-based architectures, multimodal AI integration, and the potential role of RAG in decentralized and federated learning environments.
