Pragmatic AI - Core Principles

Version 0.0.1

Pragmatic AI applies battle-tested software development principles to the world of artificial intelligence. Instead of chasing the latest trends or implementing AI for its own sake, Pragmatic AI focuses on delivering systems that solve real problems effectively, securely, and ethically.

1. Value-Driven Development

• Focus on delivering real business value rather than implementing AI for its own sake
• Ask “Does this AI solution solve an actual problem?” before proceeding
• Measure success by outcomes, not model complexity

2. Explainability by Default

• Prioritize approaches where decisions can be understood by stakeholders
• Consider explainability as a primary feature, not a nice-to-have
• Balance performance with transparency appropriately

3. Security by Design

• Build security considerations into AI systems from the ground up
• Protect against adversarial attacks, data poisoning, and prompt injection
• Implement proper authentication and authorization for AI services

4. Principle of Least Privilege

• AI systems should only access data and systems absolutely necessary
• Minimize potential damage from security compromises
• Compartmentalize access based on specific functions

5. Start Simple, Iterate Often

• Begin with the simplest solution that could possibly work
• Add complexity only when simpler approaches prove insufficient
• Continuous improvement over big-bang deployments

6. Tracer Bullet Development

• Build end-to-end thin slices of functionality early
• Validate that your AI solution integrates properly with existing systems
• Establish a learning pipeline from the beginning

7. Design for Testability

• Create AI systems where outputs can be verified objectively
• Define clear performance metrics and baselines
• Test not just for accuracy, but for bias, robustness, and edge cases

8. Data Pragmatism

• Recognize that data quality often matters more than algorithm sophistication
• Spend more time on data preparation than model tuning
• Understand your data’s limitations and biases

9. Orthogonality

• Design AI components with clear, limited responsibilities
• Create systems that can be developed, tested, and maintained independently
• Minimize unexpected interactions between components

10. Reversibility

• Make architectural choices that don’t lock you into specific AI frameworks
• Prepare for rapidly evolving technology and approaches
• Design for model swappability

Pragmatic AI was inspired by the Pragmatic Programming movement and extends those principles to the world of artificial intelligence.