From Strategy and Design to Real-World Applications Pulapaka, Sanjeev. creator author. aut http://id.loc.gov/vocabulary/relators/aut Godavarthi, Srinath. author. aut http://id.loc.gov/vocabulary/relators/aut Ding, Sherry. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text xxu 2024 1st ed. 2024. monographic eng

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access XXII, 309 p. 31 illus., 30 illus. in color. online resource. This is your guide book to Generative AI (GenAI) and its application in addressing real-world challenges within the public sector. The book addresses a range of topics from GenAI concepts and strategy to public sector use cases, architecture patterns, and implementation best practices. With a general background in technology and the public sector, you will be able to understand the concepts in this book. The book will help you develop a deeper understanding of GenAI and learn how GenAI differs from traditional AI. You will explore best practices such as prompt engineering, and fine-tuning, and architectural patterns such as Retrieval Augmented Generation (RAG). And you will discover specific nuances, considerations, and strategies for implementation in a public sector organization. You will understand how to apply these concepts in a public sector setting and address industry-specific challenges and problems by studying a variety of use cases included in the book in the areas of content generation, chatbots, summarization, and program management. What You Will Learn GenAI concepts and how GenAI differs from traditional AI/ML Prompt engineering, fine-tuning, RAG, and customizing foundation models Strategy, methodologies, and frameworks for the public sector Public sector use cases in the areas of content generation, summarization, and chatbots, plus program management, analytics, business intelligence, and reporting Architecture and design patterns Implementation, operations, and maintenance of GenAI applications. Chapter 1: Introduction to Generative AI -- Chapter 2: Generative AI in the Public Sector -- Chapter 3: Gen AI Strategy: A Blueprint for Successful Adoption -- Chapter 4: Building a Generative AI Application -- Chapter 5: Content Generation -- Chapter 6: Chatbots and Enterprise Search -- Chapter 7: Summarization -- Chapter 8: Program Management, Business Intelligence, and Reporting -- Chapter 9: Implementation Considerations -- Chapter 10: Conclusion -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E. by Sanjeev Pulapaka, Srinath Godavarthi, Sherry Ding. Artificial intelligence Machine learning Python (Computer program language) Cloud Computing Artificial Intelligence Machine Learning Python Cloud Computing Q334-342 TA347.A78 9798868804731 https://doi.org/10.1007/979-8-8688-0473-1 https://doi.org/10.1007/979-8-8688-0473-1 240728 20251030112656.0 979-8-8688-0473-1