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OpenAI API Bible Volume 1

Foundations of GPT and the OpenAI Ecosystem

Simple: if you read this book, you'll learn how to build AI applications. If you don’t... well, nothing happens. But if you do... get ready, because you’ll want to automate the world.

Improve your programming skills

What You'll Get from This Book

7 chapters spanning over 610 pages

More than 210 explanatories blocks of code

More than 30 practical exercises

2 Quizzes to test your knowledge

About thIS book

I’m tired of hearing that AI is changing the world.

Is that really true? What do you think?

I believe it's not AI that's changing the world — it’s the people who program it.

That’s who’s truly behind the change. Programming AI, of course.

Do you want to be one of those people changing the world?

When I started teaching myself to code (way back in the early 2000s), a lot of things were different. Chat rooms were still a thing. Now it’s all about Tinder, Snapchat, and TikTok.

Or so I’ve heard — I’m not on any of those.

I’m more of a Facebook–X–Instagram kind of guy. Grandpa tech, with my 42 years.

Back then, PHP and MySQL ruled the scene. Now it’s Python, NodeJS, and NoSQL databases :(

At that time, people were saying a new era was beginning — “the computer era.”

Eventually, that became the “Internet era.”

Because the Internet is what gave computers their true purpose.

Makes sense, right?

Now we’ve been three years deep into what everyone’s calling the “AI era.”

And this time, I think the name is going to stick.

But there’s one key difference between the “Computer Era,” the “AI Era,” and the people who changed the world.

Back then, to learn how to code, you either had to be rich enough to go to MIT or spend hours digging through Yahoo with a dial-up connection (1–2 kb/s), usually ending up stuck or giving up.

Today, information is everywhere — even on TikTok. And with 100 Mbps speeds.

There’s so much info, in fact, it’s overwhelming.

You can get lost in it. Or paralyzed.

Back then, only a few people managed to change the world through programming.

Now? A lot more could — if programming excites you.

And now, with APIs, everything is so much easier.

Take the OpenAI API, for example. You can build actual, functional AI applications that solve real problems in the time it takes you to finish reading this page.

Don’t believe it? Here are three examples:

1. A natural language assistant

Answers questions like a pro — with just a few lines of code.

import openai

openai.api_key = "YOUR_API_KEY"

def answer(question):
   response = openai.ChatCompletion.create(
       model="gpt-4o",
       messages=[{"role": "user", "content": question}]
   )
   return response.choices[0].message.content

print(answer("What is machine learning?"))

2. An audio-to-text transcriber with Whisper

Upload audio, get clean text. Great for interviews, lectures, podcasts.

import openai

audio = open("audio.mp3", "rb")
transcription = openai.Audio.transcribe("whisper-1", audio)
print(transcription["text"])

3. A creative social media post generator

Turn basic ideas into attention-grabbing posts — instantly.

import openai

idea = "Promote an online AI course for beginners"
response = openai.ChatCompletion.create(
   model="gpt-4o",
   messages=[
       {"role": "system", "content": "Turn ideas into engaging social media posts."},
       {"role": "user", "content": idea}
   ]
)
print(response.choices[0].message.content)

That easy. That fast. That useful.

Now imagine what you could do if you learned all this properly.

Would anything be off-limits?

How much could you be earning?

Do these code snippets make you want to build something useful with AI?

If you already know how to do this — or if you're building much more advanced stuff — then honestly, this book isn’t for you.

Maybe Volume 2. Or Volume 3.

But if you want to learn, and you answered “Yes” to the question above —

(In case you forgot, it was “Do you want to be one of those people changing the world?”)

Then you’re in the right place. Don’t go anywhere.

To save you dozens of Google searches and countless hours of YouTube (and a few wasted on TikTok), we’ve distilled everything you need to know to “change the world with AI programming” into three volumes — so you don’t have to spend a fortune, just piggyback on the billions $$$$$$$$$$$$$$$$$$ OpenAI already invested.

This is the first one.

OpenAI API Bible – Volume 1: Foundations of GPT and the OpenAI Ecosystem

What will you find in this book?

I’ll tell you in a second.

But first — you can buy it right here (cheaper), or grab it on Amazon.

This book was written for people who already know some programming.

But that doesn’t mean it’s not for you — it just means you need to know a bit of Python. The basics.

The book takes care of the rest: learning to use the OpenAI API to build real AI apps.

(If you don’t know the basics of Python yet, no problem. Start here or here)

Then back to here. Remember it!

Into the technical stuff? Here you go:

This first volume covers everything you need to build smart, useful AI-powered tools:

✅ How to set up your environment

✅ How to use GPT, Whisper, DALL·E, and Embeddings

✅ How to structure API calls

✅ How to build multi-turn conversations with memory

✅ How to implement function calling and tool chaining

✅ How to secure your keys and avoid billing surprises

✅ How to think like a product builder, not just a coder

Each chapter includes hands-on exercises. As it should.

Because hey — the idea is to write code, right?

Who is this book for?

  • Developers who want to build smarter products
  • Creators who want to automate and scale
  • Entrepreneurs who want to launch fast
  • Students looking for real-world AI skills
  • Anyone tired of watching tutorials and never applying them
  • Or maybe... just for you

What’s next?

This is Volume 1 of a 3-part series:

  • Volume 1: Foundations — models, prompts, memory, chatbots
  • Volume 2: Audio, vision, and multimodal apps
  • Volume 3: Deployment, vector databases, LangChain, mobile, and full-stack AI

Start with this one.

Read it. Code with it.

Use it to build things that matter. Things that change the world.

Or just for fun — you’ll change the world later.

Below you'll find the full chapter list. Take a look.

We’ve also added answers to some common FAQs.

You don’t have to read them. But you could.

And if you have a question — reach out.

If not... read the book.

See you soon.

I mean it.

M.A. Gonzalez

P.S.

If you’ve read this far, you already know what to do.

Don’t overthink it.

Read the book, write a few lines of code...

And start building things that — who knows — might one day show up on TechCrunch.

Or just earn you some nice extra cash.

Either way, totally worth it.

Because we’re living in the AI era — and those who know how to use the OpenAI API are the ones building the tools that are reshaping everything: education, business, healthcare, entertainment. If you're not learning this now, you're already late.
Because it teaches you how to think like a product builder, not just a coder. You’ll stop writing abstract code and start building apps that solve real problems — fast, clean, and with AI baked right in.
No fluff. No theory marathons. You learn by doing — with mini-projects, real-world examples, and code you can copy, run, and build on. This book was written for action-takers, not readers.
Just a bit of Python. That’s it. If you can write a function and know what a loop is, you’re good to go. If not, we’ll guide you to the right starting point.
Got a question? We’re just a click away.

Table of contents

Chapter 1: Welcome to the OpenAI Ecosystem

1.1 Introduction to OpenAI and Its Capabilities

1.2 Use Cases Across Industries

1.3 The Evolution of OpenAI’s Models

Chapter 1 Summary

Chapter 2: Getting Started as a Developer

2.1 Creating Your OpenAI Account and API Key

2.2 Setting Up Your Environment (Python, Node.js, Postman, Curl)

2.3 API Documentation Tour

2.4 Managing Your API Keys Securely

Practical Exercises Chapter 2

Chapter 3: Understanding and Comparing OpenAI Models

3.1 GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, and GPT 4.5

3.2 Lightweight Models — o3-mini, o3-mini-high, gpt-4o-mini, and More

3.3 Model Capabilities and Limitations

3.4 Performance, Pricing, Token Limits

Practical Exercises — Chapter 3

Quiz Part I

Questions

Answers

Chapter 4: The Chat Completions API

4.1 Roles: System, User, and Assistant

4.2 Structure of API Calls

4.3 Using max_tokens, stop, and Streaming Outputs

Practical Exercises — Chapter 4

Chapter 4 Summary

Chapter 5: Prompt Engineering and System Instructions

5.1 Crafting Effective Prompts

5.2 Using System Messages Strategically

5.3 Prompt Templates: Coding, Productivity, Customer Support

5.4 Few-shot, Zero-shot, and Chain-of-Thought Prompting

Practical Exercises — Chapter 5

Chapter 6: Function Calling and Tool Use

6.1 Introduction to Function Calling

6.2 Defining Functions and Parameters

6.3 Tool Use and API Chaining

6.4 Introduction to Retrieval-Augmented Generation (RAG)

6.5 Responses API Overview

Chapter 7: Memory and Multi-Turn Conversations

7.1 Short-Term vs Long-Term Memory

7.2 Thread Management and Context Windows

7.3 Storing and Retrieving Past Interactions

7.4 Context Limit Workarounds

7.5 Comparing Chat Completions vs Assistants API

ES Quiz Part II

Questions

Answers

Reviews

What our readers are saying about this book

Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.

Recommended by dozens of people
Review from Amazon

Megan T.

I’ve bought more AI books than I care to admit… and most of them are 80% theory and 20% confusion. This one? Total opposite. In the first pages I built a chatbot, transcribed an audio file, and created a content generator I now use for my freelance clients. The writing is clear, human, and funny — like having a smart friend show you how this stuff works.

Review from Amazon

Carlos

It covers the essential models — GPT, Whisper, DALL·E, embeddings — and walks through structured implementations step by step. The examples are real, not toy problems, and the author does a great job explaining why things work, not just how. Highly recommended for developers looking to add AI skills to their toolbox without getting lost in academic noise.

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