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

Summary

In this chapter, we discussed the concept of fine-tuning within the ChatGPT API, exploring how it can help us to tailor ChatGPT API responses to our specific needs. By training a pre-existing language model on a diverse dataset, we enhanced the davinci model performance and adapted it to a particular task and domain. Fine-tuning enriched the model’s capacity to generate accurate and contextually fitting responses by incorporating domain-specific knowledge and language patterns. Throughout the chapter, we covered several key aspects of fine-tuning, including the available models for customization, the associated costs, data preparation using JSON files, the creation of fine-tuned models via the OpenAI CLI, and the utilization of these models with the ChatGPT API. We underscored the significance of fine-tuning to achieve superior outcomes, reduce token consumption, and enable faster and more responsive interactions.

Additionally, the chapter offered a comprehensive...