Chapter 1, Getting Started with Generative AI, defines the key terminology associated with GenAI and introduces the components of the AI/ML stack. It also briefly covers the evolution of AI and the benefits, risks, and ethics of AI solutions.

Chapter 2, Building Blocks of Intelligent Applications, provides an overview of the logical and technical building blocks of intelligent applications, exploring the core structures that define intelligent applications and how these components function to create dynamic, context-aware experiences.

Chapter 3, Large Language Models, covers the main components of a modern transformer-based LLM, providing a quick overview of the LLM landscape as it stands today and introducing methods that can help you make the most of your LLM.

Chapter 4, Embedding Models, is an in-depth exploration of embedding models. It explains the different types of embedding models and how you can choose the one most suited to your requirements.

Chapter 5, Vector Databases, explores the power of vector databases for AI applications by detailing the concept of vector search and sharing case studies and best practices on using vector databases to enhance user experience.

Chapter 6, AI/ML Application Design, covers the key aspects of designing AI/ML applications. You will learn how to effectively manage data storage, flow, freshness, and retention in a secure and efficient manner.

Chapter 7, Useful Frameworks, Libraries, and APIs, explores the ecosystem of frameworks, libraries, and APIs crucial for building AI applications, helping you experiment with some of these for your own use case.

Chapter 8, Implementing Vector Search in AI Applications, covers the power of retrieval-augmented generation (RAG) to enhance AI capabilities. It uses practical examples to help you tap into the strengths of vector search.

Chapter 9, LLM Output Evaluation, explores concepts and methods for assessing the quality of LLM output. It discusses various evaluation techniques and metrics to ensure accurate, coherent, and relevant output.

Chapter 10, Refining the Semantic Data Model to Improve Accuracy, explores strategies to refine your semantic data model to improve retrieval accuracy for vector searches in RAG applications and ensure better outputs.

Chapter 11, Common Failures of Generative AI, delves into the common pitfalls of AI systems and provides strategies for overcoming them, exploring issues such as hallucinations, data leakage, cost optimization, and performance bottlenecks.

Chapter 12, Correcting and Optimizing Your Generative AI Application, discusses several techniques for enhancing the performance of GenAI applications, detailing each technique and explaining them with practical examples.