Sources and Attribution

**Author:** Sayed Allam **Last Updated:** June 20, 2025 This document provides detailed attribution and sourcing information for all prompts included in this collection, ensuring proper credit...

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**Author:** Sayed Allam **Last Updated:** June 20, 2025 This document provides detailed attribution and sourcing information for all prompts included in this collection, ensuring proper credit to original creators and transparency about data sources. --- ## 🎯 Attribution Philosophy We believe in **transparent and ethical prompt sharing** that: - Credits original creators and researchers - Respects intellectual property rights - Promotes open knowledge sharing - Maintains academic and professional integrity - Supports the AI research community --- ## 📚 Source Categories ### 1. Official Company Releases Prompts officially disclosed or released by AI companies: #### OpenAI Sources - **Official Documentation**: OpenAI platform documentation and guides - **Research Papers**: Published academic papers and technical reports - **Developer Resources**: Official API documentation and examples - **Public Statements**: Blog posts and official communications #### Anthropic Sources - **Constitutional AI Papers**: Research publications on AI safety - **Claude Documentation**: Official product documentation - **Safety Research**: Published safety and alignment research - **Technical Blog Posts**: Official Anthropic blog content #### Google Sources - **Gemini Documentation**: Official Google AI documentation - **Research Publications**: Google AI research papers - **Developer Guides**: Google Cloud AI platform resources - **Official Announcements**: Product launch and update announcements #### Microsoft Sources - **Copilot Documentation**: Official GitHub Copilot and Microsoft Copilot docs - **Azure AI Resources**: Microsoft Azure AI platform documentation - **Research Papers**: Microsoft Research publications - **Official Blog Posts**: Microsoft AI blog content ### 2. Leaked or Disclosed Prompts Prompts that became public through various disclosure methods: #### Disclosure Methods - **User Reverse Engineering**: Community efforts to understand system prompts - **Accidental Disclosure**: Unintentional exposure through system responses - **Security Research**: Ethical security research and responsible disclosure - **Developer Sharing**: Shared by developers with permission or authorization #### Verification Status - **Verified**: Confirmed authentic through multiple sources - **Likely Authentic**: Strong evidence of authenticity - **Community Reported**: Shared by community, verification pending - **Historical**: No longer current but historically significant ### 3. Open Source Projects Prompts from open source AI projects and repositories: #### GitHub Repositories - **Awesome Prompts**: Community-curated prompt collections - **LangChain**: Prompt templates from the LangChain project - **Individual Repositories**: Personal and project-specific prompt collections - **Research Projects**: Academic and research institution repositories #### Community Contributions - **Reddit Communities**: r/ChatGPT, r/MachineLearning, r/PromptEngineering - **Discord Servers**: AI development and prompt engineering communities - **Twitter/X Threads**: Shared by researchers and developers - **Professional Networks**: LinkedIn posts and professional sharing ### 4. Academic and Research Sources Prompts from academic research and educational institutions: #### Research Papers - **ACL Conferences**: Association for Computational Linguistics publications - **NeurIPS**: Neural Information Processing Systems papers - **ICML**: International Conference on Machine Learning papers - **ArXiv Preprints**: Pre-publication research papers #### Educational Institutions - **University Research**: Academic institution AI research projects - **Course Materials**: Educational prompt examples and templates - **Thesis Work**: Graduate student research on prompt engineering - **Workshop Materials**: Academic workshop and conference materials --- ## 🔍 Verification Process ### Source Verification Steps 1. **Origin Identification**: Trace back to original source 2. **Authenticity Check**: Verify through multiple independent sources 3. **Date Verification**: Confirm creation and publication dates 4. **Context Validation**: Ensure proper context and intended use 5. **Legal Review**: Check licensing and usage permissions ### Quality Assurance - **Cross-Reference**: Compare with known authentic sources - **Expert Review**: Validation by domain experts when possible - **Community Feedback**: Leverage community knowledge and verification - **Ongoing Monitoring**: Regular updates and corrections as needed --- ## 📊 Detailed Source Breakdown ### Category 1: Leaked System Prompts (Major AI Models) - 82 Prompts #### OpenAI Model Prompts (25 prompts) | Model | Source Type | Verification | Date Range | |-------|-------------|--------------|------------| | ChatGPT-4o | User reverse engineering | Verified | 2024-2025 | | ChatGPT-4o Mini | Community disclosure | Likely authentic | 2024-2025 | | DALL-E 3 | Official documentation | Verified | 2023-2024 | | Deep Research | Security research | Verified | 2024-2025 | | O3/O4 Models | Early access leaks | Community reported | 2024-2025 | #### Anthropic Model Prompts (18 prompts) | Model | Source Type | Verification | Date Range | |-------|-------------|--------------|------------| | Claude 3.5 Sonnet | User reverse engineering | Verified | 2024-2025 | | Claude 3.7 | Community sharing | Likely authentic | 2024-2025 | | Constitutional AI | Research papers | Verified | 2022-2024 | | Claude API Tools | Developer documentation | Verified | 2023-2025 | #### Google Model Prompts (15 prompts) | Model | Source Type | Verification | Date Range | |-------|-------------|--------------|------------| | Gemini 2.0 | Official announcements | Verified | 2024-2025 | | Gemini 2.5 | Developer previews | Likely authentic | 2025 | | Bard Advanced | User disclosure | Community reported | 2023-2024 | | Gemini Diffusion | Research publications | Verified | 2024-2025 | #### X.ai Model Prompts (12 prompts) | Model | Source Type | Verification | Date Range | |-------|-------------|--------------|------------| | Grok 2 | Platform documentation | Verified | 2024-2025 | | Grok 3 | Early access users | Likely authentic | 2025 | | X.ai Platform | Developer resources | Verified | 2024-2025 | #### Other Model Prompts (12 prompts) | Provider | Models | Source Type | Verification | |----------|---------|-------------|--------------| | Microsoft | Bing Chat, Copilot | Official docs | Verified | | DeepSeek | Reasoning models | Research papers | Verified | | Alibaba | Qwen series | Official releases | Verified | | Moonshot | Kimi models | Community sharing | Likely authentic | ### Category 2: AI Development Tools & Platforms - 23 Prompts #### Code Editors and IDEs (12 prompts) | Tool | Version | Source Type | Verification | |------|---------|-------------|--------------| | Cursor IDE | v0.38+ | User extraction | Verified | | Windsurf | Current | Developer sharing | Likely authentic | | Replit | Coding Assistant | Official docs | Verified | | VS Code Extensions | Various | Extension code | Verified | #### Web Development Platforms (11 prompts) | Platform | Source Type | Verification | Access Method | |----------|-------------|--------------|---------------| | Bolt.new | User analysis | Verified | Public interface | | v0 (Vercel) | Developer sharing | Likely authentic | Beta access | | Lovable | Community reports | Community reported | User sharing | | Same.dev | Documentation | Verified | Public docs | ### Category 3: System Prompts Leaks by Company - 38 Prompts #### Company-Specific Collections - **OpenAI Internal** (12 prompts): Internal documentation leaks, security research - **Anthropic Research** (10 prompts): Research paper extracts, safety documentation - **Google AI** (8 prompts): Internal tool prompts, research projects - **Microsoft** (5 prompts): Office integration, enterprise tools - **Others** (3 prompts): Various companies and platforms ### Category 4: Agent Prompts Collection - 17 Prompts #### Agent Types and Sources - **Multi-Tool Agents** (5 prompts): Open source projects, research papers - **Specialized Agents** (7 prompts): Domain-specific implementations - **Workflow Agents** (3 prompts): Business process automation - **Research Agents** (2 prompts): Academic and analysis tools ### Category 5: LLMs & Agents System Prompts - 15 Prompts #### Modern LLM Sources - **Chinese Models** (8 prompts): Qwen, ChatGLM, Kimi official sources - **Research Models** (4 prompts): Academic and experimental models - **Specialized Models** (3 prompts): Domain-specific implementations ### Category 6: AI Agent Ideation Prompts - 15 Prompts #### Creative and Ideation Sources - **Community Contributions** (8 prompts): Reddit, Discord, Twitter - **Educational Resources** (4 prompts): Courses, tutorials, workshops - **Personal Projects** (3 prompts): Individual developer creations ### Category 7: AI System Prompts by Kishan Patel - 12 Prompts #### Curated Collection by Kishan Patel - **Original Source**: Kishan Patel's curated collection - **Verification**: Author-verified through original repository - **Quality**: Hand-selected high-performance prompts - **Focus**: Development and practical applications --- ## ⚖️ Legal and Ethical Considerations ### Fair Use and Educational Purpose This collection is assembled under fair use principles for: - **Educational Purposes**: Learning prompt engineering techniques - **Research Activities**: Academic study of AI system design - **Transformative Use**: Adaptation and improvement of AI systems - **Public Benefit**: Democratizing access to AI knowledge ### Intellectual Property Respect We respect intellectual property rights by: - **Proper Attribution**: Crediting all known original sources - **Educational Focus**: Using content for learning and research - **No Commercial Exploitation**: Not profiting from others' work - **Responsive Removal**: Removing content upon request from rights holders ### Privacy and Safety We maintain privacy and safety by: - **No Personal Data**: Excluding any personally identifiable information - **Safety Guidelines**: Preserving important safety and ethical guidelines - **Responsible Sharing**: Encouraging ethical use of shared prompts - **Community Standards**: Following community norms and expectations --- ## 🤝 Community Contribution ### How Sources Are Added 1. **Community Submission**: Users submit new prompts with source information 2. **Verification Process**: Sources are verified through our quality assurance process 3. **Attribution Review**: Proper attribution and licensing are confirmed 4. **Quality Assessment**: Content quality and relevance are evaluated 5. **Integration**: Approved content is integrated with full attribution ### Source Quality Standards - **Authenticity**: Must be verifiable as authentic or likely authentic - **Attribution**: Complete source information must be available - **Quality**: Must meet our quality standards for prompt effectiveness - **Relevance**: Must be relevant to the collection's educational purpose - **Legality**: Must comply with applicable laws and ethical standards --- ## 📝 Correction and Update Process ### Reporting Issues If you identify any issues with sources or attribution: 1. **Contact Information**: Email or GitHub issue with detailed information 2. **Evidence Required**: Provide evidence for any corrections needed 3. **Correction Process**: We will investigate and make necessary updates 4. **Timeline**: Most corrections will be addressed within 7 days 5. **Acknowledgment**: Corrections will be acknowledged in update logs ### Regular Updates - **Monthly Reviews**: Regular review of source information and updates - **Community Feedback**: Integration of community feedback and corrections - **New Sources**: Addition of newly discovered or released prompts - **Quality Improvements**: Ongoing improvements to verification and attribution --- ## 🔗 External Resources ### Additional Prompt Collections - [Awesome Prompts](https://github.com/f/awesome-chatgpt-prompts) - [LangChain Prompt Hub](https://smith.langchain.com/hub) - [OpenAI Examples](https://platform.openai.com/examples) - [Anthropic Prompt Library](https://docs.anthropic.com/claude/prompt-library) ### Research and Academic Sources - [Papers with Code - NLP](https://paperswithcode.com/area/natural-language-processing) - [ACL Anthology](https://aclanthology.org/) - [ArXiv AI Section](https://arxiv.org/list/cs.AI/recent) - [Google AI Publications](https://ai.google/research/pubs/) --- **Source documentation compiled by Sayed Allam - June 2025** *Ensuring transparency and proper attribution in AI knowledge sharing*

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