Book Image

Modern Generative AI with ChatGPT and OpenAI Models

By : Valentina Alto
4.9 (8)
Book Image

Modern Generative AI with ChatGPT and OpenAI Models

4.9 (8)
By: Valentina Alto

Overview of this book

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.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Generative AI and GPT Models
4
Part 2: ChatGPT in Action
11
Part 3: OpenAI for Enterprises

Road to ChatGPT: the math of the model behind it

Since its foundation in 2015, OpenAI has invested in the research and development of the class of models called Generative Pre-trained Transformers (GPT), and they have captured everyone’s attention as being the engine behind ChatGPT.

GPT models belong to the architectural framework of transformers introduced in a 2017 paper by Google researchers, Attention Is All You Need.

The transformer architecture was introduced to overcome the limitations of traditional Recurrent Neural Networks (RNNs). RNNs were first introduced in the 1980s by researchers at the Los Alamos National Laboratory, but they did not gain much attention until the 1990s. The original idea behind RNNs was that of processing sequential data or time series data, keeping information across time steps.

Indeed, up to that moment in time, the classic Artificial Neural Network (ANN) architecture was that of the feedforward ANN, where the output of each hidden...