GPT-3

Book description

GPT-3: NLP with LLMs is a unique, pragmatic take on Generative Pre-trained Transformer 3, the famous AI language model launched by OpenAI in 2020. This model is capable of tackling a wide array of tasks, like conversation, text completion, and even coding with stunningly good performance. Since its launch, the API has powered a staggering number of applications that have now grown into full-fledged startups generating business value. This book will be a deep dive into what GPT-3 is, why it is important, what it can do, what has already been done with it, how to get access to it, and how one can build a GPT-3 powered product from scratch.

This book is for anyone who wants to understand the scope and nature of GPT-3. The book will evaluate the GPT-3 API from multiple perspectives and discuss the various components of the new, burgeoning economy enabled by GPT-3. This book will look at the influence of GPT-3 on important AI trends like creator economy, no-code, and Artificial General Intelligence and will equip the readers to structure their imaginative ideas and convert them from mere concepts to reality.

Table of contents

  1. Preface
    1. Conventions Used in This Book
    2. Using Code Examples
    3. O’Reilly Online Learning
    4. How to Contact Us
    5. Acknowledgments
      1. From Sandra
      2. From Shubham
  2. 1. The Era of Large Language Models
    1. Natural Language Processing: Under the Hood
    2. Language Models: Bigger and Better
    3. The Generative Pre-Trained Transformer: GPT-3
      1. Generative Models
      2. Pre-trained Models
      3. Transformer Models
    4. A Brief History of GPT-3
      1. GPT-1
      2. GPT-2
      3. GPT-3
    5. Accessing the OpenAI API
  3. 2. Using the OpenAI API
    1. Navigating the OpenAI Playground
      1. Prompt Engineering and Design
    2. How the OpenAI API Works
      1. Execution Engine
      2. Response Length
      3. Temperature and Top P
      4. Frequency and Presence Penalties
      5. Best Of
      6. Stop Sequence
      7. Inject Start Text and Inject Restart Text
      8. Show Probabilities
    3. Execution Engines
      1. Davinci
      2. Curie
      3. Babbage
      4. Ada
      5. Instruct Series
    4. Endpoints
      1. List Engines
      2. Retrieve Engine
      3. Completions
      4. Semantic Search
      5. Files
      6. Classification (Beta)
      7. Answers (Beta)
      8. Embeddings
    5. Customizing GPT-3
      1. Apps Powered by Customized GPT-3 Models
      2. How to Customize GPT-3 for Your Application
    6. Tokens
    7. Pricing
    8. GPT-3’s Performance on Conventional NLP Tasks
      1. Text Classification
      2. Named Entity Recognition
      3. Text Summarization
      4. Text Generation
    9. Conclusion
  4. 3. Programming with GPT-3
    1. Using the OpenAI API with Python
    2. Using the OpenAI API with Go
    3. Using the OpenAI API with Java
    4. GPT-3 Sandbox Powered by Streamlit
    5. Going Live with GPT-3-Powered Applications
    6. Conclusion
  5. 4. GPT-3 as a Launchpad for Next-Generation Start-ups
    1. Model-as-a-Service
    2. The New Start-up Ecosystem: Case Studies
      1. Creative Applications of GPT-3: Fable Studio
      2. Data Analysis Applications of GPT-3: Viable
      3. Chatbot Applications of GPT-3: Quickchat
      4. Marketing Applications of GPT-3: Copysmith
      5. Coding Applications of GPT-3: Stenography
    3. An Investor’s Perspective on the GPT-3 Start-up Ecosystem
    4. Conclusion
  6. 5. GPT-3 for Corporations
    1. Case Study: GitHub Copilot
      1. How It Works
      2. Developing Copilot
      3. No-Code/Low-Code: Simplifying Software Development?
      4. Scaling with the API
      5. What’s Next for GitHub Copilot?
    2. Case Study: Algolia Answers
      1. Evaluating NLP Options
      2. Data Privacy
      3. Cost
      4. Speed and Latency
      5. Lessons Learned
    3. Case Study: Microsoft Azure OpenAI Service
      1. A Partnership That Was Meant to Be
      2. An Azure-Native OpenAI API
      3. Resource Management
      4. Security and Data Privacy
      5. Model-as-a-Service at the Enterprise Level
      6. Other Microsoft AI and ML Services
      7. Advice for Enterprises
      8. OpenAI or Azure OpenAI Service: Which Should You Use?
    4. Conclusion
  7. 6. Challenges, Controversies, and Shortcomings
    1. The Challenge of AI Bias
      1. Anti-Bias Countermeasures
    2. Low-Quality Content and the Spread of Misinformation
    3. The Environmental Impact of LLMs
    4. Proceeding with Caution
    5. Conclusion
  8. 7. Democratizing Access to AI
    1. No Code? No Problem!
    2. Access and Model-as-a-Service
    3. Conclusion
  9. Index
  10. About the Authors

Product information

  • Title: GPT-3
  • Author(s): Sandra Kublik, Shubham Saboo
  • Release date: July 2022
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098113629