Turns a rough, vague prompt into a clear one that gets better answers from an LLM.
Most people type a half-formed prompt and get a half-useful answer. PromptBridge sits between the user and the model: it works out what the user is actually trying to do, then rewrites the prompt to match. We built it in a team of four at HackByte 4.0 (IIITDM Jabalpur), where we were among the Top 120 teams in India.
- Detects the intent behind a user's prompt
- Refines the prompt using an LLM (Llama-3-70B on Groq)
- Saves sessions and prompt history so users can go back to earlier versions
Built with: FastAPI, React, PostgreSQL, Groq (Llama-3-70B)
| Part | What it does |
|---|---|
| React frontend | Where the user enters a prompt and sees the refined result |
| FastAPI backend | Receives requests and coordinates the steps below |
| Intent detection engine | Works out what the user is trying to achieve |
| Refinement pipeline | Sends the prompt and detected intent to Llama-3-70B on Groq and returns an improved prompt |
| PostgreSQL | Stores sessions and prompt history |
I designed the system architecture and owned the PostgreSQL database design, including connecting the backend to it for session and prompt-history storage. I also helped integrate everything into one working demo. My teammates built the intent-detection engine and the prompt-refinement pipeline.
You'll need Python 3.10+, Node.js 18+, PostgreSQL and a Groq API key.
git clone https://github.com/PrajyotKorde-18/HackByte4.0.git
cd HackByte4.0Backend
cd backend
pip install -r requirements.txtCreate a .env file:
GROQ_API_KEY=your_key_here
DATABASE_URL=postgresql://user:password@localhost:5432/promptbridge
uvicorn main:app --reloadFrontend
cd frontend
npm install
npm run dev- Designing the data model early made it much easier for four people to build separate parts that still fit together
- Integration at a hackathon is where things break, so agreeing on API shapes up front saves hours
- Working with a team under a deadline means splitting ownership clearly
Built at HackByte 4.0 by a team of four.
- Prajyot Korde: architecture and database design (GitHub)
- Teammates: Naman Chhallani,Yash Dhayal,Durgesh Mundada
Prajyot Korde, IT undergrad at Ramdeobaba University LinkedIn · GitHub