Compétences — vue longue

---
title: Evaluation Workflow Design
category: skills
created: 2026-12-22
updated: 2026-12-22
tags: [evaluation-workflows, development-integration, automated-assessment, best-practices, systematic-methodology]
sources: [raw/papers/the-llm-evaluation-guidebook.pdf]
confidence: high
---

# Evaluation Workflow Design

Practical skill for designing and implementing evaluation workflows that integrate seamlessly into model development processes. Combines principles from [systematic-llm-evaluation](/concepts/systematic-llm-evaluation) with operational expertise to create effective assessment pipelines.

## Design Methodology

### Requirements Analysis
- Development team workflow assessment
- Evaluation objective definition
- Resource constraint identification
- Timeline and frequency requirements

### Workflow Architecture
- Automated trigger design
- Benchmark selection and sequencing
- Result aggregation and reporting
- Feedback loop implementation

## Implementation Patterns

### Continuous Integration
```yaml
# Example evaluation pipeline trigger
on:
  model_checkpoint:
    - automated_benchmark_suite
    - capability_regression_check
    - performance_comparison
    - result_documentation

Development Integration

• Pre-commit evaluation hooks
• Automated benchmark execution
• Performance dashboard updates
• Development team notifications

Best Practices

Automation Design

• Minimal manual intervention
• Self-documenting processes
• Scalable execution infrastructure
• Reliable result aggregation

Quality Assurance

• Benchmark validation procedures
• Result verification protocols
• Error handling and recovery
• Performance monitoring

Common Challenges

Resource Management

• Computational cost optimization
• Execution time constraints
• Infrastructure scaling
• Cost-benefit balance

Integration Complexity

• Legacy system compatibility
• Team workflow adaptation
• Tool ecosystem integration
• Change management

Tools and Frameworks

• olmo-eval - Reference implementation
• continuous-evaluation - Operational philosophy
• benchmark-design - Assessment methodology

See also

• systematic-llm-evaluation - Theoretical foundation
• Model Development Loop - Development integration
• evaluation-frameworks - Assessment structure

Systematic approach to completing and delivering functional prototypes within hackathon timeframes, emphasizing technical validation, demo readiness, and professional presentation.

Completion Checklist

Technical Validation

• Syntax Checking: Run node --check on all JavaScript files
• Local Deployment: Verify complete application stack runs locally
• User Interactions: Test all interactive elements and user flows
• Cross-Browser Testing: Validate rendering in target browsers
• Error Handling: Ensure graceful degradation for edge cases

Visual Quality Assurance

• Rendering Verification: Confirm all UI components display correctly
• Responsive Design: Test on different screen sizes if applicable
• Data Visualization: Validate charts, graphs, and visual elements
• CSS Debugging: Resolve layout and styling issues
• DOM Inspection: Use browser dev tools for final verification

Documentation and Presentation

• README Creation: Write pitch-ready project documentation
• Value Proposition: Clearly articulate problem and solution
• Technical Overview: Explain architecture and key features
• Setup Instructions: Provide clear deployment/demo steps
• Next Steps: Outline future development roadmap

File Organization Pattern

hackathon-project/
├── index.html          # Main entry point
├── src/
│   ├── app.js         # Core application logic
│   └── components/    # Modular components
├── styles.css         # Visual styling
├── README.md          # Pitch documentation
├── data/              # Sample datasets
└── assets/            # Images, icons, etc.

Quality Gates

Minimum Viable Demo

1. Core Functionality: Primary use case works end-to-end
2. Stable Deployment: Can reliably demonstrate locally
3. Clear Value: Obvious benefit to target users
4. Professional Presentation: Clean, organized codebase

Enhancement Priorities

1. User Experience: Smooth interactions and clear feedback
2. Visual Polish: Professional appearance and consistent styling
3. Error Resilience: Graceful handling of edge cases
4. Performance: Reasonable response times for demo scenarios

Development Workflow

Final Hours Strategy

1. Feature Freeze: Stop adding new functionality
2. Bug Triage: Focus only on demo-breaking issues
3. Documentation Sprint: Create comprehensive README
4. Validation Loop: Systematic testing of all components
5. Demo Rehearsal: Practice presentation flow

Problem Resolution

• Isolate Issues: Test components independently
• Pragmatic Solutions: Choose reliable approaches over perfect ones
• Time Boxing: Set strict limits on debugging sessions
• Fallback Planning: Prepare alternatives for risky features

Presentation Preparation

README Template

# Project Name

## Problem
[Clear statement of user problem]

## Solution
[How your prototype addresses it]

## Demo
[Step-by-step demo instructions]

## Technical Implementation
[Architecture overview]

## Next Steps
[Future development roadmap]

Demo Environment

• Local Server: Reliable localhost deployment
• Data Preparation: Pre-loaded with compelling examples
• Browser Setup: Clean environment without extensions
• Backup Plan: Screenshots or video if live demo fails

Success Metrics

Technical Success: All core features functional and validated Demo Success: Smooth presentation with clear value demonstration
Documentation Success: Professional README enabling easy understanding Time Success: Delivered within hackathon constraints with buffer for issues

This systematic approach ensures hackathon prototypes transition from working code to professional, presentable deliverables ready for judging and future development.

---
title: Multi-Iteration Problem Solving
category: skills
created: 2025-12-30
updated: 2025-12-30
tags: [multi-iteration-problem-solving, ai-agents, development-workflows, claude-fable, complex-tasks, iterative-development, problem-decomposition, sustained-engagement, technical-challenges, workflow-orchestration]
sources: [raw/feeds/2026-06-11-initial-impressions-of-claude-fable-5.md]
confidence: high
---

# Multi-Iteration Problem Solving

Advanced AI capability demonstrated by frontier models like claude-fable 5 to maintain context and progress through complex technical problems across multiple interaction cycles, identifying sub-problems and implementing comprehensive solutions.

## Demonstrated Capabilities

### Complex Technical Upgrades
claude-fable 5 successfully upgraded micropython-wasm to full [cpython-wasm](/concepts/cpython-wasm):
1. **Initial analysis**: Identified Brett Cannon's cpython-wasi-build as solution path
2. **Implementation attempt**: First working version with directory-preopen approach
3. **Optimization cycles**: Multiple iterations on single-zip-stdlib problem
4. **Production packaging**: Final 13.9MB wheel with [uv](/concepts/uv-integration) integration

### Library Enhancement Development
Systematic development of llm-library 0.32a3:
1. **Problem identification**: Recognized need for [human-in-the-loop](/concepts/human-in-the-loop) capabilities
2. **Hack-based prototyping**: Initial working implementation using workarounds
3. **Architecture refinement**: Converting hacks to supported features
4. **Feature development**: Four distinct enhancements to tool calling system
5. **Release preparation**: Complete release notes and documentation

## Key Characteristics

### Context Preservation
- **Long-term memory**: Maintaining problem context across multiple interactions
- **Goal tracking**: Remembering original objectives while pursuing sub-goals
- **State awareness**: Understanding current progress and remaining tasks
- **Relationship mapping**: Tracking dependencies between different problem aspects

### Problem Decomposition
- **Sub-problem identification**: Breaking complex tasks into manageable components
- **Priority assessment**: Understanding which problems to tackle first
- **Dependency analysis**: Recognizing how sub-problems relate to each other
- **Scope management**: Knowing when to expand or constrain problem scope

### Iterative Refinement
- **Solution evolution**: Improving approaches through multiple attempts
- **Hack elimination**: Converting quick fixes into robust implementations
- **Quality improvement**: Progressive enhancement of solution quality
- **Documentation development**: Creating comprehensive explanations of final solutions

## Implementation Patterns

### Progressive Problem Solving
1. **Initial working solution**: Get basic functionality working first
2. **Problem space exploration**: Understand limitations and edge cases
3. **Architecture improvement**: Refactor for better design patterns
4. **Feature completion**: Add remaining functionality and polish
5. **Documentation and packaging**: Prepare for production use

### Sustained Engagement Indicators
- **Solution persistence**: Continuing work despite initial setbacks
- **Alternative exploration**: Trying multiple approaches to same problem
- **Quality focus**: Not settling for quick fixes when better solutions possible
- **Comprehensive completion**: Seeing problems through to production-ready state

## Technical Applications

### Development Workflow Integration
- **Repository analysis**: Understanding codebases through exploration
- **Package management**: Installing and integrating dependencies
- **Build system navigation**: Working with complex build requirements
- **Testing and validation**: Ensuring solutions work in real environments

### Complex System Development
- **Multi-component systems**: Coordinating changes across multiple parts
- **API integration**: Working with external services and dependencies
- **Error handling**: Developing robust failure recovery mechanisms
- **Performance optimization**: Iterative improvement of system performance

## Success Factors

### Model Capabilities
- **Knowledge depth**: Understanding of technologies and best practices
- **Pattern recognition**: Identifying similar problems and solutions
- **System thinking**: Understanding how components interact
- **Quality standards**: Knowing difference between hacks and proper solutions

### Human Collaboration
- **Goal clarity**: Clear communication of desired outcomes
- **Scope flexibility**: Willingness to expand scope when beneficial
- **Feedback integration**: Incorporating human guidance and preferences
- **Trust building**: Allowing model to pursue comprehensive solutions

## Practical Benefits

### Development Acceleration
- **Reduced debugging time**: Comprehensive solutions reduce future problems
- **Better architecture**: Multi-iteration development produces cleaner code
- **Knowledge capture**: Solutions come with detailed explanations
- **Reusable patterns**: Solutions designed for broader applicability

### Problem Complexity Handling
- **Large problem decomposition**: Breaking down overwhelming tasks
- **Cross-domain integration**: Solving problems spanning multiple technologies
- **Legacy system upgrade**: Modernizing existing systems with new capabilities
- **Performance optimization**: Systematic improvement of existing solutions

## Best Practices

### Problem Presentation
- **Clear objectives**: Define desired end state explicitly
- **Scope boundaries**: Indicate what changes are acceptable
- **Quality standards**: Specify requirements for production readiness
- **Context provision**: Share relevant background and constraints

### Collaboration Approach
- **Patience with iteration**: Allow time for comprehensive solutions
- **Scope evolution**: Accept that good solutions may expand scope
- **Feedback timing**: Provide input at appropriate points in process
- **Trust in process**: Let model work through complex problem spaces

Multi-iteration problem solving represents a significant advancement in AI capabilities, enabling models to tackle real-world complexity that requires sustained focus and progressive refinement.

## See also
- claude-fable
- [Complex Development Workflows](/concepts/parallel-development-workflows)
- [tool-calling](/concepts/tool-calling)
- [human-in-the-loop](/concepts/human-in-the-loop)
- Workflow Orchestration