دانلود کتاب سیستم‌های RAG مبتنی بر هوش مصنوعی مولد: طراحی سیستم‌های هوش مصنوعیِ آگاه از بافت و مقیاس‌پذیر

Artificial intelligence has entered a new phase. For years, systems were designed to analyze data, detect patterns, and make predictions. Today, they can do something fundamentally different. They can generate—producing language, explanations, and solutions that feel natural and, at times, remarkably human. This shift has transformed how we interact with technology. Tasks that once required deep expertise can now be accelerated through intelligent systems. Developers write code with assistance. Analysts explore insights conversationally. Organizations begin to rely on AI not just for automation, but for augmentation. And yet, with all this progress, a critical challenge remains. Generative systems are powerful, but they are not inherently reliable. They can produce answers that sound correct without being grounded in real data. They can be helpful, but not always accurate. In environments where precision matters—financial systems, enterprise platforms, production workflows—this gap becomes a limitation that cannot be ignored. This book is built around a simple but important idea: Intelligence must be grounded to be trusted. Retrieval-Augmented Generation (RAG) represents one of the most practical approaches to achieving this. By combining generative models with structured retrieval, it allows systems to move beyond general knowledge and operate within real, domain-specific contexts. But RAG is not just a technique. It is a way of thinking about system design. The chapters that follow explore this idea from multiple angles.