Book Image

Exploring GPT-3

By : Steve Tingiris
Book Image

Exploring GPT-3

By: Steve Tingiris

Overview of this book

Generative Pre-trained Transformer 3 (GPT-3) is a highly advanced language model from OpenAI that can generate written text that is virtually indistinguishable from text written by humans. Whether you have a technical or non-technical background, this book will help you understand and start working with GPT-3 and the OpenAI API. If you want to get hands-on with leveraging artificial intelligence for natural language processing (NLP) tasks, this easy-to-follow book will help you get started. Beginning with a high-level introduction to NLP and GPT-3, the book takes you through practical examples that show how to leverage the OpenAI API and GPT-3 for text generation, classification, and semantic search. You'll explore the capabilities of the OpenAI API and GPT-3 and find out which NLP use cases GPT-3 is best suited for. You’ll also learn how to use the API and optimize requests for the best possible results. With examples focusing on the OpenAI Playground and easy-to-follow JavaScript and Python code samples, the book illustrates the possible applications of GPT-3 in production. By the end of this book, you'll understand the best use cases for GPT-3 and how to integrate the OpenAI API in your applications for a wide array of NLP tasks.
Table of Contents (15 chapters)
1
Section 1: Understanding GPT-3 and the OpenAI API
4
Section 2: Getting Started with GPT-3
8
Section 3: Using the OpenAI API

Chapter 6: Content Filtering

In Chapter 1, Introducing GPT-3 and the OpenAI API, we briefly mentioned that a content filtering model is available to recognize potentially offensive or harmful language. We also discussed the fact that GPT-3 will, at times, generate completions that some may find inappropriate or hurtful. In this chapter, you will learn how to implement content filtering to prevent users of your application from seeing offensive or potentially harmful completions.

The topics we will be covering in this chapter are as follows:

  • Preventing inappropriate and offensive results
  • Understanding content filtering
  • Testing the content filtering process
  • Filtering content with JavaScript
  • Filtering content with Python