Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.What you will learn Understand the architecture and components of the generative AI stack Explore the role of vector databases in enhancing AI applications Master Python frameworks for AI development Implement Vector Search in AI applications Find out how to effectively evaluate LLM output Overcome common failures and challenges in AI development Who this book is for This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
In this book, two distinguished philosophers debate one of the most controversial public policy issues of the late 20th century. Each begins by making a case for or against affirmative action, laying out the major arguments on both sides. Each author then responds to the other's essay. Written in an engaging, accessible style, Affirmative Action is an excellent text for junior level philosophy, political theory, public policy, and African-American studies courses as well as a guide for professionals navigating this important debate.
Coverage includes the new legal definitions that are coming into use with respect to climate change, emissions trading and the regulation of greenhouse gases.
Tracing the full history of traditionally white college fraternities in America from their days in antebellum all-male schools to the sprawling modern-day college campus, Nicholas Syrett reveals how fraternity brothers have defined masculinity over the course of their 180-year history. Based on extensive research at twelve different schools and analyzing at least twenty national fraternities, The Company He Keeps explores many factors--such as class, religiosity, race, sexuality, athleticism, intelligence, and recklessness--that have contributed to particular versions of fraternal masculinity at different times. Syrett demonstrates the ways that fraternity brothers' masculinity has had consequences for other students on campus as well, emphasizing the exclusion of different groups of classmates and the sexual exploitation of female college students.
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