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Prompt Engineering and Template Management Pattern

A governance pattern that centralizes prompt design, versioning, and optimization across an organization. This pattern ensures consistency, enables prompt reuse, and provides systematic approaches to prompt improvement and testing.

When to Use

Use this pattern when multiple teams are using similar prompts, when you need consistent prompt quality across applications, or when prompt performance optimization is critical. Essential for large organizations with multiple AI applications and when prompt intellectual property needs protection.

Architecture Drawing

The architecture shows a centralized prompt management system that stores, versions, and optimizes prompts, with testing capabilities and integration points for various applications.

Components: - Prompt Repository (300, 50) - Prompt Designer Interface (150, 120) - Version Control System (300, 120) - Template Engine (450, 120) - A/B Testing Framework (300, 200) - Performance Analyzer (450, 200) - Application Integration (150, 280) - LLM Services (300, 280) - Metrics Dashboard (450, 280)

Edges: - Prompt Designer Interface → Prompt Repository: "New Prompts" - Prompt Repository → Version Control System: "Prompt Versions" - Version Control System → Template Engine: "Template Retrieval" - Template Engine → A/B Testing Framework: "Test Variants" - A/B Testing Framework → LLM Services: "Test Prompts" - LLM Services → Performance Analyzer: "Response Quality" - Performance Analyzer → Metrics Dashboard: "Analysis Results" - Application Integration → Template Engine: "Prompt Requests" - Metrics Dashboard → Prompt Designer Interface: "Optimization Insights"

Model Versioning Pattern, Content Generation Pipeline Pattern, Quality Assurance Pattern

References

  1. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models - 2022 - arXiv - Foundational research on structured prompting techniques for improved reasoning
  2. Constitutional AI: Harmlessness from AI Feedback - 2022 - Anthropic - Advanced prompting techniques for building more helpful, harmless, and honest AI systems
  3. Prompt Engineering Guide - 2023 - Community Resource - Comprehensive collection of prompt engineering techniques and best practices