Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI
Key Features
• Explore the theory behind generative AI models and the road to GPT3 and GPT4
• Become familiar with ChatGPT's applications to boost everyday productivity
• Learn to embed OpenAI models into applications using lightweight frameworks like LangChain
Book Description
Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data.
Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster.
You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications.
By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects.
What you will learn
• Understand generative AI concepts from basic to intermediate level
• Focus on the GPT architecture for generative AI models
• Maximize ChatGPT's value with an effective prompt design
• Explore applications and use cases of ChatGPT
• Use OpenAI models and features via API calls
• Build and deploy generative AI systems with Python
• Leverage Azure infrastructure for enterprise-level use cases
• Ensure responsible AI and ethics in generative AI systems
Who this book is for
This book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models.
A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.
Author(s): Valentina Alto
Edition: 1
Publisher: Packt Publishing
Year: 2023
Language: English
Commentary: Publisher's PDF
Pages: 286
City: Birmingham, UK
Tags: Artificial Intelligence; Machine Learning; To Read; Natural Language Processing; Python; Marketing; A/B Testing; Research; Search Engine Optimization; GPT-3; ChatGPT; GPT-4; Generative AI; Prompt Design
Cover
Title Page
Copyright and credits
Contributors
Table of Contents
Preface
Part 1: Fundamentals of Generative AI and GPT Models
Chapter 1: Introduction to Generative AI
Introducing generative AI
Domains of generative AI
Text generation
Image generation
Music generation
Video generation
The history and current status of research
Summary
References
Chapter 2: OpenAI and ChatGPT – Beyond the Market Hype
Technical requirements
What is OpenAI?
An overview of OpenAI model families
Road to ChatGPT: the math of the model behind it
The structure of RNNs
The main limitations of RNNs
Overcoming limitations – introducing transformers
GPT-3
ChatGPT: the state of the art
Summary
References
Part 2: ChatGPT in Action
Chapter 3: Getting Familiar with ChatGPT
Setting up a ChatGPT account
Familiarizing yourself with the UI
Organizing chats
Summary
References
Chapter 4: Understanding Prompt Design
What is a prompt and why is it important?
Zero-, one-, and few-shot learning – typical of transformers models
Principles of well-defined prompts to obtain relevant and consistent results
Avoiding the risk of hidden bias and taking into account ethical considerations in ChatGPT
Summary
References
Chapter 5: Boosting Day-to-Day Productivity with ChatGPT
Technical requirements
ChatGPT as a daily assistant
Generating text
Improving writing skills and translation
Quick information retrieval and competitive intelligence
Summary
Chapter 6: Developing the Future with ChatGPT
Why ChatGPT for developers?
Generating, optimizing, and debugging code
Generating documentation and code explainability
Understanding ML model interpretability
Translation among different programming languages
Summary
Chapter 7: Mastering Marketing with ChatGPT
Technical requirements
Marketers’ need for ChatGPT
New product development and the go-to-market strategy
A/B testing for marketing comparison
Boosting Search Engine Optimization (SEO)
Sentiment analysis to improve quality and increase customer satisfaction
Summary
Chapter 8: Research Reinvented with ChatGPT
Researchers’ need for ChatGPT
Brainstorming literature for your study
Providing support for the design and framework of your experiment
Generating and formatting a bibliography
Generating a presentation of the study
Summary
References
Part 3: OpenAI for Enterprises
Chapter 9: OpenAI and ChatGPT for Enterprises – Introducing Azure OpenAI
Technical requirements
OpenAI and Microsoft for enterprise-level AI – introducing Azure OpenAI
Microsoft AI background
Azure OpenAI Service
Exploring Playground
Why introduce a public cloud?
Understanding responsible AI
Microsoft’s journey toward responsible AI
Azure OpenAI and responsible AI
Summary
References
Chapter 10: Trending Use Cases for Enterprises
Technical requirements
How Azure OpenAI is being used in enterprises
Contract analyzer and generator
Identifying key clauses
Analyzing language
Flagging potential issues
Providing contract templates
Frontend with Streamlit
Understanding call center analytics
Parameter extraction
Sentiment analysis
Classification of customers’ requests
Implementing the frontend with Streamlit
Exploring semantic search
Document embedding using LangChain modules
Creating a frontend for Streamlit
Summary
References
Chapter 11: Epilogue and Final Thoughts
Recap of what we have learned so far
This is just the beginning
The advent of multimodal large language models
Microsoft Bing and the Copilot system
The impact of generative technologies on industries – a disruptive force
Unveiling concerns about Generative AI
Elon Musk calls for stopping development
ChatGPT was banned in Italy by the Italian “Garante della Privacy”
Ethical implications of Generative AI and why we need Responsible AI
What to expect in the near future
Summary
References
Index
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