AI is everywhere now.
Developers use it to debug code. Designers use it to generate concepts. Founders use it to validate ideas. Marketers use it to write campaigns. Students use it to learn faster. Creators use it to produce images, videos, scripts, and music.
But there is still one annoying problem:
Most people are rewriting prompts from scratch every single day.**
The same debugging prompt.
The same landing page prompt.
The same SEO prompt.
The same product strategy prompt.
The same image generation prompt.
The same resume prompt.
The same study prompt.
So I built Prompt Empir — a professional, open-source AI prompt library designed to make high-quality prompts easier to discover, copy, improve, and share.
GitHub repo: https://github.com/gavuvapro/prompt-empire
What is Prompt Empir?
Prompt Empir is a curated open-source collection of reusable prompts for major AI tools and real professional workflows.
It is built for:
- Developers
- Designers
- Founders
- Marketers
- Students
- Researchers
- Writers
- Content creators
- Freelancers
- Businesses
- AI enthusiasts
The goal is simple:
Make prompt engineering practical, organized, reusable, and open-source.
Instead of keeping your best prompts scattered across Notion, Google Docs, random chats, and browser bookmarks, Prompt Empir gives them a clean home.
Why I built it
There are many AI prompt lists online, but most of them have the same problems:
- They are not organized for serious work.
- They are hard to search.
- They mix beginner prompts with professional prompts.
- They are not designed for contribution.
- They do not cover enough AI platforms.
- They do not feel like real open-source projects.
I wanted something that feels closer to:
- Awesome Lists
- Hugging Face documentation
- Vercel-style design
- Tailwind UI quality
- GitHub-readable Markdown
A prompt library should not just be a list of random phrases.
A good prompt is a reusable workflow.
It should include context, constraints, role, expected output, use cases, and example results.
That is what Prompt Empir is trying to standardize.
Supported AI platforms
Prompt Empir is not only for ChatGPT.
It includes prompts for many major AI systems, including:
General AI
- ChatGPT
- Claude
- Gemini
- Grok
- DeepSeek
- Perplexity
Coding AI
- Cursor
- GitHub Copilot
- Windsurf
- Replit AI
- Bolt
- Lovable
Image AI
- Midjourney
- DALL-E
- Flux
- Stable Diffusion
- Leonardo AI
Video AI
- Veo
- Runway
- Kling
Audio AI
- ElevenLabs
- Suno
- Udio
This makes the project useful for people working across text, code, design, image generation, video generation, and audio generation.
Categories inside the library
Prompt Empir is organized by professional use case, not just by tool.
Current categories include:
- Development
- Frontend
- Backend
- React
- Next.js
- Expo
- Python
- Node.js
- APIs
- Databases
- DevOps
- Security
- AI Engineering
- Design
- UI Design
- UX Design
- Branding
- Logo Design
- Business
- Startups
- SaaS
- Product Strategy
- Marketing
- Sales
- Writing
- Blogging
- SEO
- Copywriting
- Education
- Research
- Productivity
- Psychology
- Philosophy
- Sports
- Finance
- Career
- Social Media
- E-commerce
- Creative Writing
- Image AI
- Video AI
- Audio AI
The idea is to make it easy to find prompts based on what you are trying to do.
Not just:
“Give me a ChatGPT prompt.”
But:
“Give me a prompt to review a Next.js API route for security issues.”
Or:
“Give me a prompt to create a SaaS pricing strategy.”
Or:
“Give me a prompt to generate a cinematic Runway video ad shot list.”
Every prompt follows a clean format
Each prompt is written in Markdown and follows a consistent structure:
# Prompt Title
## Description
Explain what the prompt does.
## Best AI Models
- ChatGPT
- Claude
- Gemini
## Use Cases
- Example 1
- Example 2
## Prompt
[prompt here]
## Example Output
[sample output]
This makes the repository easy to read on GitHub and easy to convert into a website.
It also makes contributions easier because everyone follows the same standard.
Example prompt
Here is the kind of prompt structure the project encourages:
# Production API Design Review
## Description
Reviews REST or GraphQL API designs for correctness, security, scalability, and developer experience.
## Best AI Models
- ChatGPT
- Claude
- DeepSeek
- GitHub Copilot
## Use Cases
- Validate endpoint design
- Improve API documentation
## Prompt
You are a principal backend architect. Review this API design for resource modeling, authentication, authorization, validation, pagination, errors, idempotency, rate limiting, observability, and versioning.
Provide a concise scorecard and improved endpoint examples.
API draft:
{{api_draft}}
Business requirements:
{{requirements}}
## Example Output
Scorecard: Authentication 8/10, Authorization 5/10.
Recommendation: add object-level authorization checks for GET /projects/{id} and use cursor pagination for list endpoints.
Notice that this is not just “write an API prompt.”
It gives the AI a role, a task, evaluation areas, context placeholders, and a clear output expectation.
That is what makes prompts more reusable.
The website experience
Prompt Empir is not only a GitHub folder.
It also includes a website built with:
- Next.js
- TypeScript
- Tailwind CSS
- Markdown content
- Fuse.js search
- Lucide icons
- Vercel deployment support
The website supports:
- Browse by category
- Full-text search
- Filter by AI model
- Copy prompts with one click
- Prompt detail pages
- Markdown rendering
- Dark mode, light mode, and system theme
- SEO metadata
- Sitemap
- Robots.txt
- Open Graph previews
This means the project can work both as:
- A GitHub open-source repository
- A polished public prompt library website
Why open-source?
Prompt quality improves when people can inspect, discuss, edit, and contribute.
Open-source makes this possible.
Anyone can:
- Fork the repository
- Add prompts
- Improve existing prompts
- Suggest categories
- Fix documentation
- Build integrations
- Use it for their own teams
- Deploy their own version
The contribution flow is simple:
git clone https://github.com/gavuvapro/prompt-empire.git
cd prompt-empire
Then:
- Fork the repository.
- Create a new branch.
- Add a prompt in the right category.
- Follow the prompt format.
- Submit a pull request.
The project uses the MIT License, so it is friendly for learning, remixing, and professional use.
Who can use this?
Developers
Use prompts for:
- Code reviews
- API design
- Security audits
- Debugging
- React refactors
- Next.js SEO
- Database schema reviews
- DevOps incident postmortems
- RAG evaluation plans
Designers
Use prompts for:
- UI design briefs
- UX research interviews
- Branding
- Logo concepts
- Typography systems
- Posters and flyers
Founders and businesses
Use prompts for:
- Startup idea validation
- SaaS pricing
- Product requirements documents
- Marketing campaigns
- Sales discovery calls
- Customer support macros
Writers and creators
Use prompts for:
- Blog outlines
- SEO content briefs
- Landing page copy
- Storytelling
- Newsletters
- Technical writing
- Video scripts
Students and researchers
Use prompts for:
- Active recall
- Study planning
- Research questions
- Concept explanations
- Language learning
AI artists and media creators
Use prompts for:
- Midjourney
- DALL-E
- Flux
- Stable Diffusion
- Leonardo AI
- Runway
- Veo
- Kling
- ElevenLabs
- Suno
- Udio
What makes a good prompt?
One lesson I learned while building this project:
A good prompt is not just a command. It is a structured request.
The best prompts usually include:
- A role
- A goal
- Context
- Constraints
- Input placeholders
- Output format
- Evaluation criteria
- Examples
Bad prompt:
Write a landing page.
Better prompt:
Act as a senior conversion copywriter.
Write landing page copy for {{product}} targeting {{audience}}.
Include hero headline, subheadline, benefits, objections, social proof, FAQ, and CTA variants.
Use a clear, confident, non-hype tone.
That difference matters.
The first prompt gives you generic output.
The second prompt gives the AI a job, a user, a structure, and a quality bar.
Why this project can help the AI community
AI tools are becoming more powerful, but many people still do not know how to communicate with them effectively.
Prompt Empir can help by giving people reusable examples they can learn from.
It can become:
- A learning resource
- A productivity tool
- A community-driven prompt database
- A starter kit for AI workflows
- A reference for better prompt design
- A base for teams building internal AI systems
The long-term vision is to make professional prompting less mysterious and more practical.
Roadmap ideas
Some possible future improvements:
- More prompts from the community
- Prompt quality ratings
- Prompt testing examples
- Prompt version history
- Industry-specific collections
- Team prompt packs
- CLI search
- Browser extension
- Prompt linting
- More AI model-specific optimization
- Multilingual prompts
- Community showcase
If you have ideas, open an issue or submit a PR.
How to support the project
If you like the idea, the best way to support it is simple:
- Star the repository.
- Share it with someone who uses AI.
- Contribute one useful prompt.
- Open an issue with feedback.
- Fork it and build your own version.
GitHub repo:
https://github.com/gavuvapro/prompt-empire
Final thought
AI is changing how we work, but better tools alone are not enough.
We also need better workflows.
Prompts are becoming part of how people think, build, learn, design, sell, research, and create.
So instead of keeping good prompts hidden in private chats, I think we should make them easier to share, improve, and reuse.
That is the idea behind Prompt Empir.
If this sounds useful, check it out, star the repo, and contribute a prompt:
https://github.com/gavuvapro/prompt-empire
You got an idea or insight? Leave it in the comment section.
Let’s build the most useful open-source prompt library together.