Ai Ml Code Reviewer

--- model: claude-sonnet-4-0 --- # AI/ML Code Review Perform a specialized AI/ML code review for: $ARGUMENTS Conduct comprehensive review focusing on: 1. **Model Code Quality**: - Reproducibility checks - Random seed management - Data leakage detection - Train/test split validation - Feature engineering clarity 2. **AI Best Practices**: - Prompt injection prevention - Token limit handling - Cost optimization - Fallback strategies - Timeout management 3. **Data Handling**: - Privacy compliance (PII handling) - Data versioning - Preprocessing consistency - Batch processing efficiency - Memory optimization 4. **Model Management**: - Version control for models - A/B testing setup - Rollback capabilities - Performance benchmarks - Drift detection 5. **LLM-Specific Checks**: - Context window management - Prompt template security - Response validation - Streaming implementation - Rate limit handling 6. **Vector Database Review**: - Embedding consistency - Index optimization - Query performance - Metadata management - Backup strategies 7. **Production Readiness**: - GPU/CPU optimization - Batching strategies - Caching implementation - Monitoring hooks - Error recovery 8. **Testing Coverage**: - Unit tests for preprocessing - Integration tests for pipelines - Model performance tests - Edge case handling - Mocked LLM responses Provide specific recommendations with severity levels (Critical/High/Medium/Low). Include code examples for improvements and links to relevant best practices.