System Prompts that will help your organization with AI integration

When I started digging into AI system prompts out of my own curiosity, I wonder what is under the hood of popular AI tools like Cursor, Lovable, Vercel and others.

I found this github repo that has exactly what I was looking for — a collection of the “system prompts” used by various AI applications. These aren’t the prompts you type in; they’re the hidden instructions developers give the AI beforehand.

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Think of system prompts as the AI’s core programming or rulebook. They tell the AI:

• How to act: Should it be a helpful assistant, a coding guru, a creative partner?
• The rules: What it can and can’t do (important guardrails!).
• How to answer: Should responses be formatted in a certain way (like code blocks)?
• What tools it can use: Sometimes, prompts explain how to use external tools or data.
• Its main goal: What is the AI fundamentally trying to achieve?

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The repo gathers these prompts for several well-known AI tools, including:

• Vercel’s v0: A tool that generates UI code from prompts. Its system prompt shows how it turns plain language into code.
• Cursor AI: An AI code editor. Its prompts detail how it understands your codebase to help out.
• Devin AI: Billed as an “AI software engineer.” Its prompt hints at how it tries to tackle complex coding tasks.
• Others like Manus, Same.dev, Lovable, Replit Agent: Each has unique instructions tailored to its specific job, showing the different ways AI is being used.

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Why I was looking for this stuff? Well, It is more than just curiosity. Why even bother digging up these hidden instructions? It turns out they’re useful for a lot of people:

• Better Prompting: If you work with LLMs, studying these advanced system prompts is like getting real-world examples of effective prompt engineering. You can borrow techniques for your own projects or even just get better results from everyday AI tools.
• Understanding AI Behavior: Ever get a weird answer from an AI? The system prompt might explain why. Knowing the rules helps predict responses, understand limitations, and figure out why things sometimes go off track.
• Product Design Insights: Building your own AI features? Analyzing these prompts reveals how others handle things like user interaction, safety, and defining core functions.
• Research Material: For AI researchers, this collection offers data to study trends in AI instruction, alignment techniques, and how LLMs are used in practice.
• AI Transparency: As AI gets more powerful, understanding its underlying instructions is key for trust and responsible development. This project is a small step towards opening up the “black box.”