Description:

Generative AI is a groundbreaking form of artificial intelligence that swiftly creates contest in response to prompts. The book offers a comprehensive and academically structured introduction to Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs). It presents the fundamental concepts, mathematical intuition, and architectural principles behind modern generative AI technologies in a clear and systematic manner. The book covers key topics such as transformers, large language models, prompt engineering, retrieval augmented generation, generative adversarial networks, diffusion models, multimodal AI, vision language models, and synthetic data generation. Rather than focusing on heavy programming or complex implementation, the book emphasizes conceptual understanding, theoretical depth, and system-level thinking, making advanced AI topics accessible to learners and researchers.

Through well-organized chapters and structured explanations, the book helps readers understand how generative models learn, generate content, and solve real-world problems across text, image, and multimodal domains. It also discusses evaluation techniques, ethical considerations, bias, and the future directions of generative AI, enabling students to develop a responsible and research-oriented perspective toward AI technologies. Overall, the book builds a strong foundation in the principles and architectures of generative AI systems.

KEY FEATURES

• Delivers a rigorous and future-oriented foundation in Generative AI, blending conceptual clarity with scientific precision.

• Provides a clear and insightful distinction between generative and discriminative paradigms with strong analytical depth.

• Traces the evolution of artificial intelligence from rule-based systems to advanced generative architectures.

• Presents a structured exploration of modern generative models, including transformers, GANs, VAEs, and diffusion frameworks.

• Integrates essential mathematical foundations, including probability theory, optimization, and information theory.

• Explains advanced training methodologies such as fine-tuning and retrieval-augmented generation techniques.

• Showcases real-world applications across healthcare, finance, education, media, and automation.

• Critically examines ethical challenges, limitations, and risks associated with Generative AI systems.

• Outlines the modern Generative AI ecosystem, including tools, platforms, and infrastructure.

• Prepares readers for future innovation with an industry-aligned and application-driven perspective on AI.

TARGET AUDIENCE

• B.Tech Computer Science and Engineering

• B.Tech. Artificial Intelligence

• B.Sc./M.Sc. Computer Science