Modern Generative AI with ChatGPT and OpenAI Models : Leverage the Capabilities of OpenAI's LLM for Productivity and Innovation with GPT3 and GPT4

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 Purchase of the print or Kindle book includes a free PDF eBook 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. Part 1: Fundamentals of Generative AI and GPT Models 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 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 Getting Familiar with ChatGPT Setting up a ChatGPT account Familiarizing yourself with the UI Organizing chats Summary References 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 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 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 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 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 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 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 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