
Learn to use the OpenAI API key with Postman, test endpoints, and explore models like GPT-3.5, GPT-4, DALL-E, and Whisper, from scratch.
Explore fundamental artificial intelligence terms such as machine learning, neural networks, deep learning, prompts, tokens, and tokenizer with practical examples and cost implications.
Learn basic prompt engineering for effective interactions with AI tools like ChatGPT. Apply five practical examples in a complete one-hour course to ask clear questions and use AI tools professionally.
Create an OpenAI platform account by navigating to platform.openai.com or the playground, signing up with email and phone OTP verification, then access API keys and sign in.
Install Postman, sign up for a free account, and choose between the web version or desktop app to access your workspace and manage your APIs.
Create and securely save your OpenAI API key, copy the secret key, and never share it; manage billing with free credits and add a payment method.
Learn how to set up a Postman collection for the OpenAI API, configure variables and bearer token authentication, test endpoints, and verify models like GPT-3.5 turbo.
Set up and test the chat completion endpoint using Postman, supply the GPT 3.5 turbo model and a messages array, and verify a 200 response from the OpenAI API.
Learn how postman acts as a client sending http requests to the OpenAI API server, using get and post methods with URL, headers, and body to produce a response.
Explore how JSON acts as a universal data language for APIs, humans, and servers, covering key-value structure, quotes, curly brackets, objects, arrays, and syntax rules.
Define GPT as a pre-trained transformer that understands and generates text. Explain how transformer, pre-trained, and generative components combine to power models like ChatGPT and GPT.
OpenAI is a research company that creates AI tools, including ChatGPT, Dall-E, and Whisper. ChatGPT uses the GPT model, illustrating the relationship between OpenAI and its products.
Navigate OpenAI documentation and compare GPT 3 with GPT 4 and GPT 4 turbo, including cost considerations. Explore Dali and whisper models, and note the GPT 3 model is obsolete.
Explore how completions generate text based on a prompt, and how crafting prompts guides AI models like ChatGPT to produce outputs such as articles, code, or chats.
Explore the OpenAI playground, a no-code interactive interface to test chat models, tune parameters, and view outputs. Learn prompt design and quick prototyping for the ChatGPT API course.
Learn to set up and test chat completion in Postman using GPT-3.5 Turbo, including system, user, and assistant roles, and how to prototype with playground before calling the API.
learn how to use postman to run chat completion with GPT-4, verify availability, compare GPT-4 and GPT-3.5 turbo, and understand API key usage and training data up to April 2023.
Learn to generate images with the dalle model by supplying text prompts and using the create image endpoint in postman with a JSON body.
Learn to transcribe audio with the whisper API using Postman by uploading a file, selecting the whisper model, and sending a post request within a 25 mb limit.
Learn to design precise prompts in your playground, use system prompts to guide responses, and adjust temperature settings to influence output quality and variety.
Balance creativity and accuracy by adjusting the temperature; explore 0 to 2 range, its impact on creative versus unreliable responses, and how to set temperature via API or the playground.
Explore how OpenAI pricing is determined by token usage across models like GPT-3.5 turbo and GPT-4 turbo, and learn to estimate costs using prompt and completion tokens.
Learn to check OpenAI service status on the official status page, monitor API, ChatGPT labs, and playground, and review past incidents to know if services are operational.
Explore how GPT 3.5 turbo powers a playground conversation with system, user, and assistant roles, and see how previous messages shape responses in postman and OpenAI api contexts.
Learn to build a GPT-3.5 turbo chat with a system prompt in Postman, configure system, user, and assistant roles, and test Ford Motors Corporation car responses.
Learn how ai detectors assess ai-generated content, and use write as human tools to convert ChatGPT outputs into human-written, SEO-friendly text that bypasses detectors.
Compare Postman and Insomnia as API clients, outlining their features, teamwork, speed, and pricing. Choose Insomnia for fast, simple setups or Postman for large teams and complex API projects.
If you want to become a confident OpenAI developer who can work with real APIs instead of just watching demos, this course is made for you.
Most people learn ChatGPT by typing prompts in a browser. That is useful, but it does not teach you how developers actually connect AI to apps, websites, dashboards, or business tools. This course takes you beyond the chat screen and shows you how to work directly with OpenAI using the Postman app and real API requests.
You will start from the basics and move step by step into real developer workflows. You will learn how OpenAI APIs work, how requests are sent, how responses come back, and how to control what the AI returns. By the end, you will be able to test, adjust, and run ChatGPT and other OpenAI models like a working developer, not just a user.
This course is designed to feel practical from the first section. You will see how to set up your OpenAI account, create API keys, and connect everything inside Postman. Then you will send live requests to ChatGPT, image tools, and transcription tools so you can see how different inputs change the output in real time.
You will also learn the key terms that developers use every day, such as HTTP, JSON, tokens, models, and temperature. These are often skipped or explained in complex ways. Here, they are broken down in plain language so you always know what you are doing and why it works.
As you move forward, you will learn how to work with GPT 3.5 Turbo and GPT 4, how to test different prompts in the Playground, and how to control cost and usage by understanding tokens and pricing. You will also learn how to read API responses and fix common errors that stop beginners from moving ahead.
What makes this course different is that it does not stop at theory. You will practice real tasks like:
Sending chat requests to OpenAI from Postman
Creating image requests with DALL·E
Sending audio files for transcription with Whisper
Adjusting settings to control how creative or direct the AI should be
Testing and tracking API results before moving to code
If you skip learning these skills, you may stay stuck relying on tools made by others instead of creating your own AI-powered systems. You may also struggle in developer roles, client work, or projects where real API knowledge is expected.
By the end of this course, you will be able to speak the language of OpenAI developers with confidence. You will know how to test ideas quickly, fix problems, and prepare your AI setup for real apps, SaaS tools, or client projects.
This course is a strong fit for anyone who wants to move into AI development, automation, or software testing roles. It also works well for founders, freelancers, and product teams who want full control over how ChatGPT and OpenAI tools work inside their systems.
If your goal is to stop guessing and start working with OpenAI like a real developer, this course will give you the skills and structure to do exactly that.