Engineering Approaches: Workflow Modes and Model Specialisation

[will-lamerton]

Engineering Approaches: Workflow Modes and Model Specialization

Hi everyone! I wanted to open up a discussion around two key areas that could significantly enhance Nanocoder's capabilities:

1. Predefined Workflow Modes

Currently, nanocoder operates with a general-purpose approach, but we could implement specialized workflow modes that follow established engineering best practices:

Proposed Workflow Modes:

Research → Plan → Implement (Anthropic's recommendation)

• Research phase: Analyze codebase, gather requirements, understand context
• Planning phase: Break down tasks, identify dependencies, create implementation strategy
• Implementation phase: Execute the plan with validation checkpoints

Spec-Driven Development

• Requirements gathering and specification writing
• Design review and approval workflow
• Implementation against spec with compliance checking

Test-Driven Development (TDD)

• Test writing phase with requirement analysis
• Red-Green-Refactor cycle management
• Automated validation at each step

Exploratory/Debugging Mode

• Hypothesis-driven investigation
• Systematic elimination and validation
• Root cause analysis with documentation

Implementation Approach:

Each mode could be implemented through:

• System prompt engineering - Mode-specific prompts that guide the AI's approach
• Workflow state management - Track progress through workflow phases
• Tool orchestration - Different tool usage patterns per mode
• Validation checkpoints - Phase-specific quality gates

2. Local Model Specialization & Routing

This is where things get really exciting for performance and cost optimization:

Task-Specific Model Routing:

Bash Command Generation

• Fine-tuned small models (1-7B parameters) specialized for shell commands
• Fast inference for common development tasks
• Fallback to frontier models for complex scenarios

Code Analysis & Refactoring

• Models trained specifically on code understanding and transformation
• Specialized for specific languages/frameworks

Documentation Generation

• Models optimized for technical writing and documentation
• Context-aware README/comment generation

Technical Implementation Ideas:

Intent Classification Router

User Input → Intent Classifier → Specialized Model Selection → Execution

Confidence-Based Fallback

• Small model attempts task first
• Confidence scoring determines if escalation to larger model is needed
• Cost/performance optimization through intelligent routing

Local Fine-Tuning Pipeline

• Collect successful task completions as training data
• Periodic fine-tuning of task-specific models
• User-specific model adaptation over time

Questions for Discussion:

1. Workflow Modes: Which workflows would be most valuable? Should these be user-selectable or automatically detected based on task type?

2. Model Architecture: How should we structure the model router? Should it be rule-based, ML-based, or hybrid?

3. Local Model Integration: What's the best way to integrate local specialized models alongside the existing multi-provider system?

4. Training Data: How can we ethically and effectively collect training data for fine-tuning without compromising user privacy?

5. Performance Metrics: How should we measure the effectiveness of workflow modes and model routing decisions?

Technical Considerations:

• Backward Compatibility: Ensure existing workflows aren't disrupted
• Configuration Management: How to manage mode selection and model preferences
• Resource Management: Balance between local compute and API calls
• Failure Handling: Graceful degradation when specialized models fail

Next Steps:

I'm particularly interested in:

• Feedback on the proposed workflow modes
• Technical approaches for model routing implementation
• Community interest in contributing to fine-tuning efforts
• Real-world use cases that would benefit most from specialization

Would love to hear everyone's thoughts! What workflows do you find yourself repeating that could benefit from structured approaches? What tasks do you think could be handled well by smaller, specialized models?

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This discussion aims to gather community input before diving into implementation. Let's design this collaboratively to ensure it serves real user needs while maintaining Nanocoder's simplicity and effectiveness.