Overview

""Model Context Protocol"" is a definitive, hands-on guide for students, developers, and researchers looking to master the next evolution of Artificial Intelligence. In an era dominated by Large Language Models (LLMs), the true differentiator for creating powerful, reliable, and valuable AI applications lies in the ability to provide them with accurate, relevant, and timely context. This book systematically demystifies this process, introducing the ""Model Context Protocol"" (MCP)-a comprehensive framework for building context-aware AI systems. Why This Book? While many books focus on the theory of AI models, this book concentrates on the practical engineering aspect of making these models useful. It is specifically designed to be the most accessible, relevant, and hands-on resource for B.Tech and M.Tech students. The content is meticulously aligned with the syllabi prescribed by AICTE and leading global universities, ensuring you learn skills that are in high demand across the industry. I replace dense academic jargon with clear explanations, intuitive analogies, and step-by-step code examples, making advanced concepts approachable for beginners while providing depth for experienced learners. Key Features of This Book: 1. Beginner to Advanced Path: A carefully curated learning curve that starts with foundational concepts and progressively builds to advanced, production-level topics. 2. Hands-On & Practical: Every chapter includes practical exercises, code snippets, and mini-projects to solidify your understanding and build your portfolio. 3. Real-World Case Studies: Explores use cases from various industries, including customer support, legal tech, research, and enterprise search, to show the real-world impact of contextual AI. 4. Complete Capstone Project: The final chapter is a comprehensive, step-by-step guide to building a fully functional AI customer support assistant, complete with a working codebase and detailed explanations. 5. Simplified Explanations: Complex topics like Transformer architecture, vector embeddings, and retrieval algorithms are broken down into simple, easy-to-digest concepts. 6. Focus on Latest Technologies: Covers the most current and relevant tools in the AI ecosystem, including Large Language Models, Vector Databases (ChromaDB), and development frameworks (LangChain, Streamlit). Who Should Read This Book? 1. B.Tech/M.Tech Students in Computer Science, IT, and AI/ML specializations. 2. Software Developers and Engineers looking to integrate advanced AI capabilities into their applications. 3. AI/ML Practitioners who want to move beyond model training and into building practical, data-driven AI systems. 4. Data Scientists seeking to leverage LLMs on proprietary or domain-specific datasets. 5. Academic Researchers exploring the fields of Information Retrieval and Natural Language Processing. 6. Tech Enthusiasts and self-learners eager to build the next generation of intelligent applications.

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