Design patterns for production prompt engineering
These patterns emerged from building and operating a 9-employee AI workforce in daily production since January 2026. They're modeled after Gang of Four design patterns -- problem/solution/example format -- because prompt engineering has matured past the "just write a good prompt" stage. Production systems need architecture, not tricks.
These aren't theoretical. They're extracted from systems running in production daily.
| # | Pattern | Description |
|---|---|---|
| 01 | Identity Separation | Decompose AI identity into independent, maintainable components |
| 02 | Session Loading | Reliable context bootstrapping for stateless AI sessions |
| 03 | Rolling Task Protocol | Prevent task staleness with time-based detection and forced decisions |
| 04 | Attention Routing | Surface critical items before routine work |
| 05 | Multi-Model Deployment | Match model capabilities and cost to task requirements |
| 06 | Employee vs Agent | Choose the right human-AI collaboration pattern for your use case |
| 07 | Persistent Memory | Structured file-based memory that survives session boundaries |
| 08 | Governance Rules | Naming conventions and structural discipline for AI-navigable file systems |
| 09 | Cross-Employee Coordination | Information sharing across multiple AI employees without confusion |
| 10 | Cultural Layer | Personality and engagement systems that improve output quality |
Each pattern follows a consistent structure:
- Problem -- The challenge you'll recognize from your own work
- Solution -- The architectural approach
- Implementation -- How to build it
- Example -- Concrete, production-tested example
- Anti-Patterns -- Mistakes we made so you don't have to
- Related Patterns -- How patterns connect to each other
- Engineers building AI-powered workflows and tools
- Teams deploying LLMs in production beyond simple chat interfaces
- Anyone who has felt the pain of prompts that work in testing but fail in production
Prompt engineering is software engineering. The same principles that make code maintainable -- separation of concerns, clear interfaces, explicit state management -- make prompts maintainable. These patterns are the proof.
prompt-engineering design-patterns ai llm best-practices