# Grammarly's CoEdIT: Production-Ready Specialized LLM for Text Editing Grammarly has developed CoEdIT, an innovative approach to deploying specialized LLMs for production text editing tasks. This case study demonstrates several key aspects of LLMOps, from dataset creation to model deployment and evaluation. ## Technical Architecture & Model Development ### Base Model Selection - Started with FLANT5 as the foundation model - Created three variants with different parameter sizes: ### Dataset Engineering - Built upon the IteraTeR+ dataset focusing on non-meaning-changing edits - Structured the dataset around specific edit categories: - Implemented systematic instruction creation: ## Production Implementation ### Model Optimization - Achieved significant model size reduction while maintaining performance - Up to 60x smaller than comparable models like GPT-3-Edit (175B parameters) - Focused on parameter efficiency through specialized training ### Deployment Strategy - Made models and data publicly available through GitHub repository - Designed for practical deployment with smaller computational requirements - Created open-source implementation for reproducibility and community adoption ## Evaluation Framework ### Comprehensive Testing Strategy - Implemented both quantitative and qualitative evaluation methods - Developed multiple comparison groups: ### Automated Evaluation - Tested against standard benchmark test sets - Evaluated multiple aspects: - Achieved state-of-the-art performance across multiple benchmarks ### Human Evaluation - Conducted expert evaluator assessments - Compared outputs between CoEdIT-XL and GPT3-Edit - Evaluated three key metrics: - Results showed 64% preference for CoEdIT vs 10% for GPT3-Edit ## Production Features ### Composite Task Handling - Developed CoEdIT-Composite for handling multi-step editing tasks - Implemented training for complex editing sequences - Enhanced model capabilities for real-world editing scenarios - Demonstrated successful handling of previously unseen task combinations ### Generalization Capabilities - Successfully handled adjacent tasks not seen during training - Demonstrated strong performance on: - Showed adaptability to new editing-related tasks ## Technical Monitoring and Quality Control ### Performance Metrics - Implemented automated evaluation pipelines - Monitored model performance across different task types - Tracked performance relative to model size - Measured computational efficiency metrics ### Quality Assurance - Established baseline quality metrics - Implemented continuous evaluation against competitor models - Maintained performance monitoring across different editing tasks - Validated outputs through human evaluation processes ## Production Challenges and Solutions ### Parameter Efficiency - Addressed the challenge of model size vs performance - Implemented specialized instruction tuning for efficiency - Achieved better results with smaller models through focused training ### Task Specificity - Solved general-purpose LLM limitations through specialization - Implemented dense task distribution approach - Created targeted instruction sets for better performance ## Future Development Roadmap ### Planned Enhancements - Working on handling longer text inputs - Improving prompt sensitivity in training and testing - Expanding composite task capabilities - Enhancing generalization to new editing tasks ### Technical Infrastructure - Focus on maintaining small model footprint - Continuing to improve instruction tuning methodology - Planning for expanded task coverage - Developing enhanced evaluation frameworks ## Production Deployment Considerations ### Resource Optimization - Significantly reduced computational requirements compared to larger models - Enabled efficient deployment with smaller infrastructure needs - Maintained high performance while reducing resource costs ### Integration Capabilities - Designed for easy integration into existing systems - Provided open-source access for community adoption - Implemented standardized interfaces for system integration ## Model Security and Ethics ### Responsible AI Implementation - Focus on specialized task completion - Reduced risk through targeted functionality - Maintained transparency through open-source approach ### Quality Control - Implemented comprehensive evaluation frameworks - Maintained performance monitoring systems - Established clear metrics for success