How to Build ChatGPT - Part 4: Vibe-Coding and Deployment
Welcome to a new series! In How to Build ChatGPT, you’ll learn step-by-step everything you need to build your very own ChatGPT application.
This will include the following topics/sessions:
Prompting / Open AI Responses API
RAG / Connectors & Data Sources
Agents / Search
End-to-End Application / Vibe-Coding the Front End & Deploying the Back-End
Reasoning / Thinking
Deep Research
Agent Mode
Each of these features is required to build our very own ChatGPT application.
Nearly three years ago, ChatGPT was released to the world - it was the fastest-growing app the world had ever seen. At the time, it was just an LLM with a front end.
Now, it’s so much more.
Meanwhile, nearly three years later in 2025, GPT-5 was recently released on the heels of gpt-oss, the first open-weight model release since GPT-2 in 2019.
We intend to follow the journey that the OpenAI product team has taken.
For aspiring AI Engineers, we believe that taking this approach to learning might be one of the best ways to learn to build 🏗️, ship 🚢, and share 🚀 customized production LLM applications for many use cases.
🛣️ Join us for the entire journey!
In Part 4, we’ll build out the front end UI (through vibe-coding!) and we’ll deploy the back end so our application can be publicly hosted.
We’ll incorporate each of the patterns we’ve learned so far, including prompt engineering, RAG/connectors, and agents/search into our end-to-end application.