Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production.

The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation). These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included.

By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business.

What You Will Learn

• What is Generative AI?
• What is ChatGPT and GPT4?
• What are language models and diffusions models?
• How do we deploy LangChain and HuggingFace?

Who This Book Is For

Software engineers with a few years of experience building applications in any language or infrastructure

Table of Contents

Chapter 1: Introduction to Generative Al

Chapter 2: LangChain: Your Swiss Army Knife

Chapter 3: Chains, Tools and Agents

Chapter 4: Guardrails and Al: Building Safe + Controllable Apps

Chapter 5: Finetuning: The Theory

Chapter 6: Finetuning: Hands on

Chapter 7: Monitoring

Chapter 8: Prompt Engineering Techniques

About the Author

Aarushi Kansal is an experienced principal engineer. She has worked in a variety of technologies, including mobile development, Python, Go, and cloud, along with booming generative AI space. She has spearheaded AI initiatives in the workplace and regularly works on creative POCs in her spare time to stay at the top of the generative AI space.