Add Sample RFE Prompt Templates Collection

[jeremyeder]

Sample RFE Prompt Templates Collection

📋 Overview

This PR introduces a structured collection of sample RFE prompt templates to support the RFE Builder system and AI agent training. The collection provides real-world examples and patterns that demonstrate best practices for RFE structure and content.

🎯 Purpose

For AI Agent Training

• Provide reference examples for agent analysis and decision-making
• Demonstrate proper RFE structure and technical depth
• Show business justification and stakeholder identification patterns
• Establish templates for risk assessment and success criteria

For Users and Contributors

• Offer comprehensive RFE examples for reference
• Document best practices for RFE creation
• Provide templates for common infrastructure scenarios
• Enable consistent RFE quality across the organization

📁 New Files Added

/prompts/samples/README.md

• Purpose: Comprehensive documentation for the sample collection
• Content: Usage guidelines, contribution standards, agent integration guidance
• Audience: Users, contributors, and AI agents

/prompts/samples/index.json

• Purpose: Structured metadata index for programmatic discovery
• Content: Sample categorization, agent usage mapping, template patterns
• Benefits: Enables AI agents to automatically discover and analyze relevant samples

/prompts/samples/rfe-examples/RHOAIRFE-159.md

• Purpose: Real-world enterprise infrastructure RFE example
• Content: Complete node targeting feature specification for OpenShift AI
• Complexity: High (34 story points, 12-16 weeks)
• Value: Demonstrates comprehensive technical and business analysis

/prompts/samples/rfe-examples/RHOAIRFE-302.md ⭐ NEW

• Purpose: Real-world user experience RFE example
• Content: Project-level resource discovery feature for OpenShift AI Dashboard
• Complexity: Medium (21 story points, 8-12 weeks)
• Value: Demonstrates multi-tenancy and self-service capabilities

🤖 AI Agent Integration

Agent-Specific Usage Patterns

Agent	Focus Areas	Relevant Sections
Parker (PM)	Prioritization & Business Impact	Business justification, stakeholder analysis, customer validation
Archie (Architect)	Technical Feasibility	Technical requirements, dependencies, implementation details
Stella (Staff Engineer)	Completeness & Quality	Success criteria, testing requirements, risk assessment
Derek (Delivery Owner)	Project Planning	Effort estimation, team assignment, deployment planning

📊 Sample Characteristics

Sample Diversity

Sample	Category	Complexity	Timeline	Focus Area
RHOAIRFE-159	Infrastructure	High	12-16 weeks	Kubernetes node targeting
RHOAIRFE-302	User Experience	Medium	8-12 weeks	Multi-tenancy & resource discovery

Template Patterns Covered

Enterprise Infrastructure (RHOAIRFE-159)

• Pattern: Large-scale infrastructure changes for enterprise customers
• Key Sections: Administrative controls, multi-tenant considerations, scalability requirements
• Use Cases: Heterogeneous hardware clusters, business unit isolation, cost optimization

User Experience Enhancement (RHOAIRFE-302)

• Pattern: UI/UX improvements focused on user productivity and self-service
• Key Sections: Use cases, user experience criteria, workflow integration
• Use Cases: Custom image development, project-specific resources, administrative flexibility

🔍 Sample Analysis

RHOAIRFE-159 (Infrastructure)

• Domain: Enterprise Kubernetes infrastructure
• Business Value: Critical for heterogeneous GPU cluster management
• Technical Risk: Medium (scheduler integration complexity)
• Teams: 3 teams (Dashboard, Platform, SRE)

RHOAIRFE-302 (User Experience) ⭐

• Domain: Multi-tenant resource management
• Business Value: High (40% increase in custom image usage expected)
• Technical Risk: Low (UI enhancements with existing APIs)
• Teams: 2 teams (Dashboard, Documentation)

🔄 Integration with Agent Observatory

This sample collection integrates with the Phase 1 Agent Observatory logging system:

• Template Usage Tracking: Monitor which samples agents reference most frequently
• Analysis Pattern Detection: Identify common agent reasoning patterns
• Quality Benchmarking: Compare new RFEs against established sample quality
• Training Data: Use samples to improve agent analysis capabilities

🚀 Future Expansion

Additional Sample Types Planned

• Feature Enhancements: Additional user-facing feature improvements
• Bug Fixes: Complex bug resolution RFEs
• Security Improvements: Security-focused infrastructure changes
• Performance Optimizations: System performance enhancement examples

Template Patterns to Add

• Microservice Architecture: Service-to-service integration patterns
• API Development: REST/GraphQL API enhancement templates
• Data Pipeline: ETL and data processing workflow examples
• Compliance: Security and regulatory compliance templates

📈 Benefits

For Development Teams

• Consistency: Standardized RFE structure and quality across infrastructure and UX domains
• Efficiency: Faster RFE creation with proven templates for different complexity levels
• Quality: Higher-quality technical specifications and business justifications

For AI Agents

• Training Data: Rich examples spanning infrastructure and user experience domains
• Pattern Recognition: Learn different approaches for high vs medium complexity features
• Context Understanding: Real-world business and technical context across categories

For Product Management

• Best Practices: Proven patterns for both complex infrastructure and user-facing features
• Risk Management: Comprehensive risk assessment templates for different domains
• Stakeholder Communication: Clear structure for cross-team alignment across complexity levels

🔍 Review Focus Areas

• Content Accuracy: Technical details and business context validation across both samples
• Sample Diversity: Effectiveness of covering both infrastructure and UX domains
• Agent Usability: Effectiveness for AI agent training across different RFE types
• Documentation Quality: Clarity and completeness of guidance materials

🤖 Generated with Claude Code

[claude]

🔍 Pull Request Review - Sample RFE Prompt Templates Collection

Thank you for submitting this comprehensive PR! I've conducted a thorough review focusing on code quality, potential issues, security, and documentation standards.

✅ Strengths

📚 Excellent Documentation Quality

• Comprehensive README.md: Well-structured with clear directory layout, usage guidelines, and contribution standards
• Detailed Index: JSON metadata file provides excellent programmatic access for AI agents
• Real-world Examples: Three diverse RFE samples covering different complexity levels and domains
• Agent Integration: Clear mapping between AI agents and relevant sections

🎯 Strong Content Organization

• Logical Structure: Hierarchical organization makes navigation intuitive
• Consistent Formatting: All markdown files follow consistent structure and styling
• Metadata Rich: Each sample includes comprehensive metadata (complexity, timeline, story points)
• Cross-referencing: Good internal linking and reference structure

🔒 Security Best Practices

• Comprehensive .gitignore: Properly excludes sensitive files (secrets, keys, environment variables)
• No Hardcoded Secrets: All sample content appears to be sanitized of sensitive information
• Safe File Types: Only documentation and configuration files, no executable code

🔍 Areas for Improvement

📐 JSON Schema Consistency

• Priority Field Inconsistency: index.json uses mixed priority formats ("P0" vs "Critical")
  • RHOAIRFE-159: "priority": "P0"
  • RHOAIRFE-302/390: "priority": "Critical"
  • Recommendation: Standardize on one format (suggest P0/P1/P2 for consistency)

📊 Sample Coverage Gaps

• Missing Low Complexity: No examples for <8 week projects
• Limited Categories: Only 3 categories represented (infrastructure, UX, model-serving)
• Recommendation: Consider adding samples for: security improvements, performance optimizations, or simple bug fixes

🔧 Minor Technical Issues

1. README.md Line 192: Version reference shows "1.1" but index.json shows "1.0" - version mismatch
2. File Path References: All samples reference rfe-examples/ but could benefit from validation that paths exist
3. Date Consistency: RHOAIRFE-390 shows creation date as "2025-01-26" which is future-dated

🚀 Performance Considerations

✅ Positive Aspects

• Static Files: All documentation files are static, no runtime performance impact
• Reasonable Size: JSON index file is well-structured and appropriately sized for parsing
• Cacheable Content: All content is cacheable and doesn't require dynamic generation

📈 Optimization Opportunities

• JSON Validation: Consider adding JSON schema validation for the index file
• Automated Testing: Could benefit from tests validating sample completeness and format consistency

🧪 Test Coverage Assessment

Current State

• Documentation-Only PR: No executable code to test
• Existing Tests: Repository has test structure (tests/ directory exists)
• Content Validation: Samples appear manually verified for accuracy

Recommendations

• JSON Schema Tests: Add validation tests for index.json structure
• Link Validation: Test that all file path references in index are valid
• Content Consistency: Automated checks for metadata consistency across samples

🛡️ Security Analysis

✅ No Security Concerns Identified

• Clean Content: All samples contain business/technical information only
• Proper .gitignore: Comprehensive exclusion of sensitive file types
• No Dependencies: This PR doesn't introduce new dependencies or attack vectors

📋 Recommendations

High Priority (Pre-Merge)

1. Fix Priority Standardization: Choose and apply consistent priority format across all samples
2. Correct Version Numbers: Align version references between README.md and index.json
3. Validate Creation Dates: Ensure sample creation dates are accurate

Medium Priority (Future)

1. Add Schema Validation: Implement automated JSON schema validation
2. Expand Sample Coverage: Add low-complexity and additional category examples
3. Content Testing: Add automated tests for sample consistency

Low Priority (Nice-to-Have)

1. Interactive Documentation: Consider adding examples of agent usage patterns
2. Template Generation: Tools to generate new samples from templates
3. Metrics Dashboard: Track which samples are most referenced by agents

🎯 Overall Assessment

Rating: ⭐⭐⭐⭐⭐ Excellent

This is a high-quality documentation PR that significantly enhances the RFE Builder system. The content is comprehensive, well-organized, and clearly serves its intended purpose for AI agent training and user reference. The identified issues are minor and easily addressed.

Recommendation: ✅ APPROVE (with minor fixes)

The PR provides substantial value and follows good documentation practices. Once the priority format inconsistencies are resolved, this will be an excellent addition to the codebase.

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Review completed using systematic analysis of code quality, security, performance, and best practices.