Patterns
Patterns are reusable techniques for getting better results from AI. They’re not tools themselves, but ways of structuring prompts, workflows, and interactions.
When to Reach for a Pattern
Section titled “When to Reach for a Pattern”- The task is ambiguous or open-ended
- A single prompt isn’t reliable enough
- You need confidence beyond “it returned something”
- You’re building a skill or workflow others will reuse
Prompting Patterns
Section titled “Prompting Patterns”How to structure what you ask.
- Chain of Thought: Force explicit reasoning steps
- Few-Shot Examples: Guide format with examples
- Structured Output: Constrain output to a schema
Verification Patterns
Section titled “Verification Patterns”How to gain confidence in output.
- Adversarial Review: Challenge output with an external skeptic
- Multi-Model Consensus: Compare independent attempts
- Self-Critique: Have the model evaluate its own output
- Verification Loops: Generate, verify, iterate
Orchestration Patterns
Section titled “Orchestration Patterns”How to structure complex work.
- Iterative Refinement: Improve output through successive passes
- Mechanical Scaffolding: Build repeatable structure once, fill with context each time
- Pipeline Orchestration: Chain sequential stages
- RAG: Ground responses in retrieved context
- Subagent Fanout: Fan work out to parallel agents
- Task Routing: Match each task to the right model for cost and capability
Evolution Patterns
Section titled “Evolution Patterns”How systems improve through use.
- Discovery Propagation: Feed improvements back to the system
- Dogfooding: Validate by using your own output
- Prompt Regression Testing: Detect when prompt changes break existing behavior