# Building Production Code Completion Tools at Gitlab ## Overview Gitlab's ModelOps team embarked on a journey to build advanced code completion tools, focusing on creating an AI-assisted development environment that truly enhances developer productivity. The project, spanning 6-7 months, demonstrates a sophisticated approach to implementing LLMs in production for code assistance. ## Core Evaluation Framework ### Three Fundamental Metrics - **Honesty**: Ensuring code completions are consistent with facts and correct - **Harmlessness**: Validating that suggestions are safe and appropriate - **Helpfulness**: Confirming that completions actually achieve the developer's goals and increase productivity ### LLM Selection Criteria - Objective alignment with code completion requirements - Parameter evaluation - Training data assessment - Existing evaluation benchmarks - Model weight flexibility - Tuning framework availability - Cost considerations - Latency requirements ## Architecture Overview ### Multi-LLM Approach - Combination of open-source pre-trained models - Integration with third-party LLMs - Ability to fine-tune and enhance models with additional data ### Data Processing Pipeline - Raw data collection from various sources (including Hugging Face) - Pre-processing and tokenization - Training environment setup - Checkpoint management ### Dual Engine System - **Prompt Engine** - **Gateway System** ## Continuous Evaluation System ### Automated Evaluation Pipeline - Token-based similarity analysis - Historic codebase pattern recognition - Developer output agreement assessment - Continuous feedback loop implementation ### Scaling Mechanisms - Microservice-based architecture - Version control integration - CI/CD pipeline implementation - Automated benchmark testing ## Reinforcement Learning Implementation ### Prompt Engineering at Scale - Template management - Dynamic prompt validation - Rate limiting - Output verification ### Continuous Improvement Loop - Starting from baseline acceptance rates - Iterative accuracy improvements - User feedback incorporation - Performance metric tracking ## Data Management and Processing ### Training Data Pipeline - Continuous data collection from actual usage - Code commit analysis - Pattern recognition - Quality assessment ### Evaluation Infrastructure - Automated similarity checking - Performance benchmarking - Quality metrics tracking - User acceptance monitoring ## Technical Implementation Details ### Microservices Architecture - Dedicated evaluation engines - Prompt management services - Model serving infrastructure - Feedback collection systems ### Version Control Integration - Code completion versioning - Prompt template versioning - Model version management - Performance tracking across versions ## Results and Impact ### Continuous Improvement Metrics - Starting from initial 10% acceptance rate - Gradual accuracy improvements through continuous learning - Enhanced developer productivity - Improved code quality ## Key Learnings ### Data-Centric Approach - Importance of continuous data collection - Quality over quantity in training data - Pattern recognition significance - User feedback integration ### Architectural Decisions - Benefits of microservices approach - Importance of scalable evaluation - Need for automated prompt engineering - Value of continuous feedback loops ## Future Directions ### Ongoing Development - Enhanced prompt engineering automation - Improved evaluation metrics - Expanded model integration - Advanced reinforcement learning implementation ## Technical Infrastructure ### System Components - Evaluation engines