دانلود کتاب راهنمای سازمانی برای پیاده‌سازی هوش مصنوعی مولد و هوش مصنوعی عامل‌دار

The advent of generative AI is reshaping the way we design our systems today. Unlike traditional AI/ML-based systems that analyze and predict, generative AI creates, whether it’s text, images, video, audio, or code; it’s changing the way we interact with data and solve problems. Fueled by large language models (LLMs), it represents a massive shift in the way enterprises now approach AI-based problem-solving. Much like the rise of big data in the early 2010s, generative AI, LLMs, and agentic AI are not just buzzwords—it is a conluence of advancements in foundation models, computing infrastructure, open source ecosystems, and cloud platforms. While the capabilities are impressive, the real value lies in applying it meaningfully within the fabric of enterprise systems—where scalability, precision, explainability, and data privacy are non-negotiable. In this book, we explore how generative AI, when combined with structured system design and responsible deployment, can become a powerful accelerator for modern enterprise solutions. From design patterns to architecture, prompt engineering to evaluation frameworks, deployment pipelines, and best practices, this book aims to be your practical guide in building production-ready GenAI applications. Our experience has taught us that implementing GenAI at scale is not just a technical challenge—it’s a multidisciplinary journey that involves thoughtful planning, risk awareness, and continuous optimization. We’ve tried to cover the ground that matters most—what works, what doesn’t, and what to watch out for. Time is valuable more so in today’s fast-paced world. We thank you for choosing to invest your valuable time in reading this book. We hope you ind its contents insightful and, most importantly, applicable to your enterprise AI journey. Wishing you the best in building secure and responsible AI solutions. Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub.