Book Image

Building AI Applications with ChatGPT APIs

By : Martin Yanev
4.2 (5)
Book Image

Building AI Applications with ChatGPT APIs

4.2 (5)
By: Martin Yanev

Overview of this book

Combining ChatGPT APIs with Python opens doors to building extraordinary AI applications. By leveraging these APIs, you can focus on the application logic and user experience, while ChatGPT’s robust NLP capabilities handle the intricacies of human-like text understanding and generation. This book is a guide for beginners to master the ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects. These projects offer practical experience in integrating ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. Throughout this book, you’ll get to grips with performing NLP tasks, building a ChatGPT clone, and creating an AI-driven code bug fixing SaaS application. You’ll also cover speech recognition, text-to-speech functionalities, language translation, and generation of email replies and PowerPoint presentations. This book teaches you how to fine-tune ChatGPT and generate AI art using DALL-E APIs, and then offers insights into selling your apps by integrating ChatGPT API with Stripe. With practical examples available on GitHub, the book gradually progresses from easy to advanced topics, cultivating the expertise required to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.
Table of Contents (19 chapters)
Free Chapter
1
Part 1:Getting Started with OpenAI APIs
4
Part 2: Building Web Applications with the ChatGPT API
8
Part 3: The ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
14
Part 4:Advanced Concepts for Powering ChatGPT Apps

Fine-Tuning ChatGPT

In this section, you will learn about the process of fine-tuning ChatGPT models. We will talk about the ChatGPT models available for fine-tuning and provide information on their training and usage costs. We will also cover the installation of the openai library and set up the API key as an environmental variable in the terminal session. This section will serve as an overview of fine-tuning, its benefits, and the necessary setup to train a fine-tuned model.

Fine-tuning enhances the capabilities of API models in several ways. Firstly, it yields higher-quality outcomes compared to designing prompts alone. By incorporating more training examples than can be accommodated in a prompt, fine-tuning enables models to grasp a wider range of patterns and nuances. Secondly, it reduces token usage by utilizing shorter prompts, resulting in more efficient processing. Additionally, fine-tuning facilitates lower-latency requests, enabling faster and more responsive interactions...