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Divyansh
Divyansh

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I Built a Prompt Forging Engine with NVIDIA Llama 3.1 70B for Google Prompt Wars 2026

What if you could take any weak, vague prompt and instantly transform it into an elite, production-ready one โ€” with a power score, technique breakdown, and warrior rank?

That's exactly what Prompt Forge does.


๐Ÿค” The Problem

Most people use AI wrong. They type something like:

"Write me a blog post about productivity"

And wonder why the output is generic. The secret isn't the model โ€” it's the prompt. A better prompt unlocks dramatically better results.

But most people don't know how to write better prompts. That's the gap Prompt Forge fills.


โš”๏ธ What is Prompt Forge?

Prompt Forge is a single-page web app that transforms weak prompts into elite, production-ready prompts using NVIDIA Llama 3.1 70B via the NVIDIA NIM API.

You paste a rough idea โ†’ hit Forge It โ†’ get back:

  • A rewritten prompt (3โ€“8ร— more detailed)
  • Which of 8 prompt engineering techniques were applied
  • A Power Level score from 1โ€“10
  • A Warrior Rank (Recruit โ†’ Bronze โ†’ Silver โ†’ Gold โ†’ Legendary)
  • A Warrior Analysis explaining every upgrade made ๐Ÿ”— Try it live โ†’

โœจ Features

  • Prompt Forging Engine โ€” rewrites weak prompts using Llama 3.1 70B Instruct
  • 8 Technique Tags โ€” Context ยท Specificity ยท Constraints ยท Output Format ยท Persona ยท Examples ยท Chain-of-Thought ยท Negatives
  • Power Level Meter โ€” 10-bar visual indicator (1โ€“10)
  • Warrior Rank System โ€” gamified ranking based on power level
  • Warrior Analysis Panel โ€” explains every improvement made
  • Typewriter Animation โ€” forged prompt streams in dramatically
  • One-click Copy โ€” instantly copy the result
  • Keyboard Shortcut โ€” Ctrl+Enter / Cmd+Enter

- Responsive โ€” works on mobile and tablet

๐Ÿ› ๏ธ Tech Stack

Layer Technology
Frontend Vanilla HTML / CSS / JavaScript
Backend Python + Flask
AI Model NVIDIA Llama 3.1 70B Instruct
API NVIDIA NIM API
Hosting Render

Deliberately kept the stack simple โ€” no React, no heavy frameworks. Just a single HTML file on the frontend and a lightweight Flask proxy on the backend.


๐Ÿง  How It Works

The architecture is straightforward:

User Input โ†’ Flask Server โ†’ NVIDIA NIM API โ†’ Llama 3.1 70B โ†’ Structured JSON โ†’ UI
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  1. User enters a weak prompt
  2. Frontend sends POST /api/forge to Flask
  3. Flask forwards to NVIDIA NIM API with a system prompt instructing Llama to act as a master prompt engineer
  4. Model returns structured JSON:
{
  "forged_prompt": "...",
  "techniques_used": ["Context", "Specificity", "Persona"],
  "power_level": 8,
  "analysis": "Added role context, specified output format..."
}
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5. Frontend renders with typewriter animation, technique tags, power meter, and warrior rank

๐Ÿ“ก The Key: System Prompt Engineering

The magic is in the system prompt sent to Llama 3.1 70B. It instructs the model to:

  • Act as a master prompt engineer
  • Identify weaknesses in the input prompt
  • Apply specific prompt engineering techniques
  • Return a structured JSON response with the forged prompt, techniques used, power level, and analysis The model essentially uses prompt engineering to improve prompts โ€” meta, but incredibly effective.

๐ŸŽ–๏ธ Warrior Rank System

Power Level Rank
1โ€“2 ๐Ÿช– RECRUIT
3โ€“4 ๐Ÿฅ‰ BRONZE
5โ€“6 ๐Ÿฅˆ SILVER
7โ€“8 ๐Ÿฅ‡ GOLD
9โ€“10 ๐Ÿ† LEGENDARY

๐Ÿš€ Run It Yourself

git clone https://github.com/Divyansh0208/Prompt_Forge.git
cd Prompt_Forge
pip install flask python-dotenv requests
cp .env.example .env
# Add your NVIDIA API key to .env
python server.py
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Get a free NVIDIA API key at build.nvidia.com.


๐Ÿ”— Links

- ๐Ÿ’ป GitHub: github.com/Divyansh0208/Prompt_Forge

Built for Google Prompt Wars 2026. Try forging your weakest prompt and see what rank you earn โš”๏ธ

Top comments (2)

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sanskriti66 profile image
Sanskriti •

Wow .. well looking at the project you've created, it looks really nice and helpful. Great work dude..๐Ÿฅฐ

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divyansh0208 profile image
Divyansh •

Thank you Sanskriti! ๐Ÿ™ Really glad you liked it.
Would love to hear what you think after trying it