Book Image

OpenAI API Cookbook

By : Henry Habib
Book Image

OpenAI API Cookbook

By: Henry Habib

Overview of this book

As artificial intelligence continues to reshape industries with OpenAI at the forefront of AI research, knowing how to create innovative applications such as chatbots, virtual assistants, content generators, and productivity enhancers is a game-changer. This book takes a practical, recipe-based approach to unlocking the power of OpenAI API to build high-performance intelligent applications in diverse industries and seamlessly integrate ChatGPT in your workflows to increase productivity. You’ll begin with the OpenAI API fundamentals, covering setup, authentication, and key parameters, and quickly progress to the different elements of the OpenAI API. Once you’ve learned how to use it effectively and tweak parameters for better results, you’ll follow advanced recipes for enhancing user experience and refining outputs. The book guides your transition from development to live application deployment, setting up the API for public use and application backend. Further, you’ll discover step-by-step recipes for building knowledge-based assistants and multi-model applications tailored to your specific needs. By the end of this book, you’ll have worked through recipes involving various OpenAI API endpoints and built a variety of intelligent applications, ready to apply this experience to building AI-powered solutions of your own.
Table of Contents (10 chapters)

Fine-tuning a completion model

Fine-tuning is the process of taking a pre-trained model and further adapting it to a specific task or dataset. The goal is typically to take an original model that has been trained on a large, general dataset and apply it to a more specialized domain or to improve its performance on a specific type of data.

We previously saw a version of fine-tuning in the first recipe within Chapter 1, where we added examples of outputs in the messages parameter to fine-tune the output response. In this case, the model had not technically been fine-tuned – we instead performed few-shot learning, where we gave examples of the output within the prompt itself to the Chat Completion model. Fine-tuning, however, is a process where a whole new subset Chat Completion model is created with training data (inputs and outputs).

In this recipe, we will explore how to fine-tune a model and execute that fine-tuned model. Then, we will discuss the benefits and drawbacks...