This content originally appeared on DEV Community and was authored by Nikhil Wagh
Introduction
Every developer wants to build something cool on weekends — but time, complexity, and boilerplate usually get in the way.
This time, I challenged myself to build an AI chatbot, powered by GPT-4, that could:
- Answer questions from a custom knowledge base
- Remember conversation context
- Be styled and responsive (Tailwind)
- Work with a .NET 10 backend for secure, authenticated users
The twist? I built it in just one weekend — with help from AI itself.
In this blog, I’ll show you how I used AI tools + full stack skills to go from zero to live chatbot with minimal effort.
Features I Wanted
My MVP had to include:
- A React-based frontend chat UI
- GPT-4-powered responses using my business data
- Backend auth with .NET 10 + JWT
- Chat history stored in a database
- Option to plug in OpenAI or Azure OpenAI
Stack I Used
| Layer | Tech |
|---|---|
| Frontend | React + Vite + TailwindCSS |
| Backend | ASP.NET Core 10 Web API |
| AI | OpenAI GPT-4 API |
| Storage | MongoDB (chat logs) |
| Hosting | Vercel (frontend), Azure App Service (API) |
How AI Helped Me Build This Faster
Here’s exactly where I used AI tools to speed up the build:
Designing the Architecture
Prompt to ChatGPT:
“Suggest a full stack architecture for a chatbot that uses OpenAI and .NET backend”
It generated:
- Auth flow with JWT
- Chat message schema
- GPT proxy service pattern
- CORS setup suggestions
Scaffolding the Backend
Prompt:
“Create an ASP.NET Core 10 Web API with login and chat controller”
Result: Fully generated controller, JWT setup, and middleware. I just cleaned up and added my logic.
React UI Generation
Prompt:
“Build a TailwindCSS-powered React chat UI with chat bubbles, input, and scroll”
Result: Working layout in 3 minutes — styled, clean, and mobile responsive.
GPT Integration Logic
Prompt:
“How to call OpenAI’s GPT-4 API from .NET Core and stream response back to frontend”
It gave me complete async streaming logic via HttpClient, chunk parser, and how to return via SignalR or HTTP chunked.
What Took the Most Time?
- Handling streaming tokens in React
- Conversation context window management
- Rate-limiting + API error handling
But even for these, AI gave me code patterns to speed things up.
Final Architecture Overview
[React UI] → [ASP.NET Core API] → [OpenAI GPT-4]
↓
[MongoDB Chat Logs]
The chatbot:
- Authenticates user via login endpoint
- Accepts user message
- Sends to OpenAI with previous context
- Streams back reply in real time
- Saves the full conversation in MongoDB
Lessons Learned
AI is now part of my workflow. Not just for generating code — but for:
- Decision making (e.g., REST vs SignalR)
- Architecture validation
- Learning unfamiliar packages (e.g., OpenAI stream parser)
I still had to:
- Review generated code
- Handle edge cases manually
- Refactor logic to fit my standards
But overall — I shipped a full product 3x faster.
Conclusion
Whether you’re building side projects, internal tools, or client features — pairing your dev skills with AI is now a superpower.
This weekend build proved to me that:
- I don’t need a big team
- I don’t need weeks
- I just need clear intent + good prompts
And yes — the chatbot is now live and being extended into a personal assistant for internal team docs.
This content originally appeared on DEV Community and was authored by Nikhil Wagh