OpenAI API Mastery: Build AI Apps with TypeScript

Name: OpenAI API Mastery: Build AI Apps with TypeScript
Rating: 4.6 (9 reviews)

Build real AI apps from scratch - ChatGPT clone, RAG chatbot, email classifier & more with OpenAI's TypeScript SDK

Created byPiotr Jura | 73,000+ Students

Last updated 8/2025

English

What you'll learn

Connect to OpenAI APIs using TypeScript and Node.js to build real AI-powered applications from basic responses to advanced function calling
Build a terminal ChatGPT clone with real-time streaming responses that works exactly like the ChatGPT interface you already know and use
Create RAG applications that let users chat with custom documents and data files, dramatically improving AI usefulness for specific domains
Implement function calling to let AI trigger real actions in your apps - users describe what they want in plain language, AI executes the code

Course content

3 sections • 19 lectures • 2h 7m total length

Introduction1:05
Need Help? How to Contact Me and Stay in Touch!0:36
Follow me on X and LinkedIn to receive updates, participate in Udemy Q&A or a direct message with me, and join the Discord to discuss with other students.
AI Models, Tokens and Cost (OpenAI)6:31
Setting Up Node.js & TypeScript + Your First AI Response6:52
Streaming AI Responses in Real Time2:42
Building a ChatGPT Clone from Scratch10:57
build a chatgpt clone from scratch that streams responses in the terminal and remembers messages via a conversation history with the system prompt 'you are a helpful ai assistant'.
Structured Output: Making AI Return JSON You Can Use14:39
Apply structured output with the OpenAI SDK to extract sentiment, produce a summary, and retrieve the customer name into a JSON object defined by a Zod schema in TypeScript.
Tool Calling: Letting AI Trigger Real Actions (Web Search)6:24

What is RAG? Concept, Workflow & Project Overview4:59
Explore retrieval augmented generation by indexing knowledge from markdown files, embedding text into vectors, and querying a vector store to supplement artificial intelligence responses with relevant documents.
RAG Chunking: Fixed-Size as the Baseline10:58
Load markdown knowledge files, discuss fixed-size chunking as the baseline, and prepare a knowledge base with document chunks ready for embeddings.
RAG Chunking: Right-Sized Chunks6:00
Create right-sized chunks from knowledge content by slicing into 1000-character segments, merging the last small chunk with the previous one, and preparing docs with file-name metadata for later embeddings.
Creating Embeddings from Chunks7:06
Query Embedding & How to Compare the Query Vector to Knowledge Base Vectors?9:21
Convert the user query into an embedding and compare it to indexed document embeddings with cosine similarity to retrieve the most relevant chunks for context in a Rag workflow.
Inserting Relevant Knowledge into the AI Response4:05
Learn how to fetch relevant documents, extract content, and inject context into user queries to improve AI responses, including embedding, vector search, and dynamic context construction.

How the Function Calling Works and What it Can Be Used For?2:44
Explore how function calling lets an AI model access your app data by defining functions like get user balance, enabling the AI to fetch current balance and return JSON responses.
Define the Tools (Available Functions to Call)6:20
Define the available tools by building a tools array in TypeScript, including a get account balance function with its parameters and strict type constraints.
Add Mock Data & Implement the Functions11:17
Figure Out Which Functions Need Calling & Call Them7:00
Determine which functions to call from OpenAI function calls, route them with a fully typed TypeScript system, and execute or mock results to complete the AI workflow.
Making Final API Call with Function Call Results7:51
Learn to implement function calling in a TypeScript app using defined tools and a router. Use type-safe arguments to handle OpenAI function calls and integrate real user data.

Requirements

Basic TypeScript knowledge

Description

Most developers think AI integration is complicated. It's not. You just need to know how to connect to the right APIs.

In this hands-on course, you'll build multiple working AI applications using TypeScript, Node.js, and the OpenAI API. No theory lectures - just real code you can use immediately.

What You'll Build:

Terminal ChatGPT Clone - Stream AI responses in real-time, just like the real thing
Email Classifier - Analyze sentiment and return structured JSON data for your apps
RAG Knowledge Chat - Let users chat with your custom documents and data files
Function Calling App - AI that can trigger real actions in your application
Plus foundational examples - Basic responses, streaming, and structured output

What Makes This Different:

I've taught almost 80,000 developers on Udemy. I don't waste your time with endless theory or marketing fluff. You'll see working code from minute one.

Each example builds practical skills you can apply immediately. The entire development and testing for this course cost me 22 cents in API calls - so you're not looking at expensive experimentation.

What You Need:

Basic TypeScript knowledge
Node.js installed
OpenAI API account (I'll show you how to set this up)
That's it

What You Get:

Multiple complete, working AI applications
All source code included with every lesson
Step-by-step setup and implementation
Real-world patterns you can use in any TypeScript project

Why TypeScript?

Because it works everywhere - Next.js, React, Node.js backends, whatever you're building. Learn once, use everywhere.

No previous AI experience required. Just bring your TypeScript skills and let's build something useful.

Stop wondering how AI integration works. Start building it.

Who this course is for:

TypeScript/JavaScript developers wanting to add AI to their apps
React, Next.js, Vue developers building AI-powered frontends
Node.js backend developers creating AI APIs
Any developer curious about practical AI integration without theory overload

OpenAI API Mastery: Build AI Apps with TypeScript

What you'll learn

Explore related topics

Course content

Introduction8 lectures • 50min

RAG: AI Chat with Your Knowledge Base6 lectures • 42min

Tools (Function Calling) - Giving AI Access to Your Application Data & Actions5 lectures • 35min

Requirements

Description

Who this course is for: