# Scaling LLM Operations at Bito: A Deep Dive into Production LLM Infrastructure ## Company Background Bito is developing an AI coding assistant that helps developers understand, explain, and generate code. The company pivoted from a developer collaboration tool to an AI-powered solution after recognizing the potential of generative AI to assist with code comprehension and development tasks. ## Technical Infrastructure ### Model Orchestration and Load Balancing - Implemented a sophisticated demultiplexing system across multiple LLM providers: - Built an intelligent routing system considering: ### Model Selection Strategy - Primary decision factors: - Fallback hierarchy: ### Vector Database and Embeddings - Currently using a homegrown in-memory vector storage solution - Repository size limitations: ### Guardrails and Quality Control - Heavy emphasis on prompt engineering for security and accuracy - Model-specific prompt variations: - Quality assurance through: ## Operational Challenges ### Rate Limit Management - Multiple accounts per provider to handle scale - Load balancer to distribute requests across accounts - Sophisticated routing logic to prevent hitting rate limits - Fallback mechanisms when primary services are unavailable ### Cost-Benefit Analysis - Chose API services over self-hosted models due to: ### Privacy and Security - Local processing of code for privacy - Index files stored on user's machine - No server-side code storage - Challenge with API providers potentially training on user data ## Best Practices and Lessons Learned ### Context is Critical - Provide maximum relevant context to models - Include specific code snippets and error messages - Structure prompts with clear instructions ### Model Management - Start with a single model until scale requires more - Add new models only when necessary due to: ### Latency Considerations - Response times vary significantly (1-15+ seconds) - Need to handle timeouts and failures gracefully - GPU availability affects performance ### Developer Experience - Experienced developers get better results due to better context provision - Important to maintain balance between automation and human expertise - Focus on providing clear, actionable responses