Generative AI in Action 289918

Generative AI in Action is the comprehensive and concrete guide to generative AI you’ve been searching for. It introduces both AI’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution.

Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale.

In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading!

Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems.

For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI.

1 Introduction to generative AI

2 Introduction to large language models

3 Working through an API: Generating text

4 From pixels to pictures: Generating images

5 What else can AI generate?

6 Guide to prompt engineering

7 Retrieval-augmented generation: The secret weapon

8 Chatting with your data

9 Tailoring models with model adaptation and fine-tuning

10 Application architecture for generative AI apps

11 Scaling up: Best practices for production deployment

12 Evaluations and benchmarks

13 Guide to ethical GenAI: Principles, practices, and pitfalls

A The book’s GitHub repository

B Responsible AI tools