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This wiki documents practical insights and strategies for effective collaboration with AI, based on real-world experience in software development and technical problem-solving.
- Providing clear context and requirements
- Understanding AI's capabilities and limitations
- The importance of human expertise in problem decomposition
- Why "lazy" approaches fail with AI
- Complementary strengths between human and AI
- Leveraging industry experience with AI capabilities
- Breaking down problems effectively
- The value of iterative refinement
- Creating persistent reference points
- Managing AI session limitations
- Building knowledge bases
- Maintaining consistency across sessions
- AI-Masterclass - Comprehensive curriculum and methodology
- Context-Building - The art of providing effective context
- Problem-Decomposition - Breaking down complex problems
- Documentation-Strategies - Creating effective reference points
- Case-Studies - Examples from actual projects
- Success-Patterns - What works and why
- Common-Pitfalls - What doesn't work and why
- Lessons-Learned - Key insights from experience
- Start with the AI-Masterclass for a structured approach
- Review Case-Studies for practical examples
- Explore Context-Building to understand effective communication
- Join our Community Discussions
This is a living document based on real-world experience. Share your insights:
- What strategies have worked for you?
- What challenges have you encountered?
- How do you leverage AI effectively?
This master class emerged from the recognition that effective AI collaboration requires:
- Clear communication strategies
- Understanding of both human and AI roles
- Practical experience in problem-solving
- Recognition of current AI limitations
This documentation is maintained by the fleXRP team and community contributors.
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