pollm design

pollm: Python Official Documentation LLM Translation Assistant

Leveraging LLMs for Technical Documentation Translation

Why Translation Matters

• Documentation is knowledge transfer
• Builds common vocabulary across languages
• Reduces information asymmetry
• Enables broader community participation
• Strengthens cross-cultural connections

Current Translation Challenges

1. Technical accuracy vs readability
2. Maintaining consistent terminology
3. Time-consuming manual review process
4. Limited translator resources
5. Context understanding requirements

Why LLMs for Translation?

• Strong language understanding capabilities
• Context-aware translations
• Consistent terminology usage
• Faster initial draft generation
• Can learn from human corrections

Learning from Aider's Success

Aider's key principles we can apply:

1. Leverage existing tools (Git, CST)
2. Focus on specific domain expertise
3. Lightweight integration
4. Clear feedback loops
5. Human-in-the-loop design

pollm Design Goals

1. Assist, not replace, human translators
2. Maintain technical accuracy
3. Ensure consistent terminology
4. Speed up initial translation process
5. Enable easy review/correction workflow

Core Components

1. Translation Engine

   • LLM integration
   • Context management
   • Terminology database

2. Review Interface

   • Diff viewing
   • Comment/correction system

Workflow Design

1. Source doc preprocessing
2. Context gathering (leveraging Aider)
3. Initial LLM translation (leveraging Aider)
4. Human review
5. Feedback incorporation (To be implemented)
6. Final verification

Technical Architecture

graph TD
    A[Source Doc] --> B[PoParser]
    B --> C[Context Manager]
    C --> D[LLM Translator]
    D --> E[Review Interface]
    E --> F[Feedback Loop]
    F --> G[Final Output]

Implementation Plan

Phase 1:

• Basic translation pipeline
• Simple review interface

Phase 2:

• Advanced context management (Feedback loop, Terminology DB)
• Terminology consistency
• Feedback incorporation

Success Metrics

1. Translation speed improvement
2. Error reduction rate
3. Reviewer satisfaction
4. Community adoption
5. Documentation coverage

Next Steps

1. Initial prototype development
2. Community feedback gathering
3. Integration with existing tools
4. Pilot testing with small docs
5. Iterative improvement