@cloudkinetix/bmad-enhanced/expansion-packs/ck-llm-agent-dev/templates/prompt-library-tmpl.md

Cloud-Kinetix enhanced fork of BMAD-METHOD - Breakthrough Method of Agile AI-driven Development with robust versioning and unified validation.

# {{Project Name}} Prompt Library

[[LLM: Research-Driven Prompt Development

RESEARCH FIRST: Before creating this prompt library:

1. Research current prompt engineering best practices and methodologies
2. Investigate domain-specific prompting patterns relevant to this project
3. Study current testing and evaluation frameworks for prompt validation
4. Research optimization techniques and performance measurement approaches
5. Analyze similar projects and their proven prompt strategies

Create a dynamic, research-informed prompt library that:

- Emphasizes discovery over static patterns
- Includes research-based validation approaches
- Documents rationale based on current best practices
- Provides guidance for continuous improvement

Output location: `docs/ai-agents/prompt-library.md`]]

## Library Overview

- **Project**: {{project-name}}
- **Last Updated**: {{date}}
- **Version**: {{version-number}}
- **Primary Models**: {{target-models}}

## Research-Driven Prompt Development

[[LLM: Before organizing prompts, research current categorization approaches and industry standards. Create categories that align with current best practices and project-specific needs.]]

### Dynamic Prompt Organization

[[LLM: Research current prompt organization patterns and create categories appropriate for this project. Consider:

1. Functional categorization (system, task-specific, reasoning, safety, etc.)
2. Performance-based organization (latency-sensitive, accuracy-critical, cost-optimized)
3. Domain-specific groupings relevant to the project context
4. Model-specific variations if working with multiple LLM providers

For each prompt category, include:

- Research-based rationale for the organization approach
- Current performance benchmarks and optimization targets
- Testing methodology and validation approaches
- Continuous improvement strategies based on latest research]]

### Research-Based Implementation Guidelines

[[LLM: Research and apply current best practices for:

1. **Prompt Structure and Design**
   - Research current effective prompt patterns and templates
   - Investigate model-specific optimization techniques
   - Study token efficiency and cost optimization strategies

2. **Testing and Validation**
   - Research current testing frameworks and methodologies
   - Investigate A/B testing approaches for prompt optimization
   - Study evaluation metrics and benchmarking standards

3. **Performance Optimization**
   - Research current optimization techniques and tools
   - Investigate cost-performance trade-offs in current market
   - Study scaling patterns and best practices

4. **Maintenance and Evolution**
   - Research version control and change management approaches
   - Investigate continuous improvement methodologies
   - Study team collaboration patterns for prompt development

Create documentation that emphasizes research-driven decision making and continuous adaptation based on current best practices.]]

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**Note**: This template emphasizes research-driven prompt development over static patterns. Always research current best practices and adapt to your specific project context and requirements.