# Building Production LLM Applications: Lessons from Weights & Biases ## Context and Background This case study presents insights from Weights & Biases, a company that has built an AI developer platform supporting ML and gen AI applications. The presentation combines both broad industry insights about LLMOps and a specific practical example of building a production-ready voice assistant. ## Current State of LLM Applications in Production - Survey of audience showed ~70% have LLM applications in production - Most implementations are custom solutions rather than purchased solutions - Nearly all Fortune 500 companies are investing in custom AI solutions - Significant gap exists between demo and production readiness ## Key Challenges in LLM Production ### The Demo-to-Production Gap - AI applications are exceptionally easy to demo but difficult to productionize - Non-deterministic nature of LLMs makes traditional software development approaches insufficient - Traditional CI/CD testing approaches don't work well for LLM applications ### IP and Knowledge Management - The learning process, not just the final model, represents the true IP - Need to preserve experimental history and learnings - Risk of knowledge loss when key personnel leave - Importance of tracking everything passively rather than relying on manual documentation ## Practical Case Study: Building an Open Source Voice Assistant ### Initial Architecture and Setup - Used open source stack including: - Designed to run on affordable hardware ($200 range) - Focus on latency optimization due to real-time requirements ### Iterative Improvement Process ### Initial Implementation - Started with basic Llama 2 implementation - Initial accuracy was 0% with default prompt - Highlighted importance of systematic improvement approach ### Improvement Steps - Basic prompt engineering - Advanced prompt engineering - Model switching - Fine-tuning ### Production Considerations and Best Practices ### Evaluation Framework - Multiple evaluation layers needed: - Metrics must correlate with actual user experience - Enterprise implementations often track thousands of metrics ### Implementation Strategy - Start with lightweight prototypes - Get early user feedback - Iterate based on metrics and user experience - Use multiple improvement techniques (prompt engineering, fine-tuning, etc.) ## Key Learnings and Best Practices ### Evaluation Best Practices - Build comprehensive evaluation framework before scaling - Include multiple types of tests - Ensure metrics align with business objectives - Make evaluation automated and reproducible ### Development Approach - Take iterative approach to improvements - Document failed experiments - Use multiple techniques in combination - Focus on real user value and experience ### Tools and Infrastructure - Need specialized tools for LLM development - Traditional software development tools often insufficient - Important to track experiments and results systematically - Consider latency and resource constraints early ## Industry Impact and Future Directions - Democratization of AI through conversational interfaces - Growth in custom AI solutions across industries - Increasing importance of software developers in AI implementation - Need for specialized LLMOps tools and practices - Balance between innovation and production readiness ## Recommendations for LLM Production Success - Build robust evaluation frameworks first - Start with lightweight prototypes - Incorporate continuous user feedback - Document everything, including failed experiments - Use multiple improvement techniques - Focus on metrics that matter to end users - Consider latency and resource constraints - Plan for iteration and improvement cycles