# Panel Discussion on LLM Evaluation and Production Deployment ## Overview This panel discussion features experts from various companies discussing practical aspects of LLM deployment and evaluation: - Josh Tobin (Gantry) - focused on LLM evaluation tools and infrastructure - Amrutha (Structured.ie) - working on LLM engineering tools - Sohini Roy (NVIDIA) - discussing Nemo Guardrails and enterprise LLM deployment ## Key Changes in LLM Deployment Landscape ### Transition from Traditional ML to LLMs - Traditional ML projects typically took 6+ months to deploy - Post-ChatGPT era enables much faster deployment (3-4 weeks) - Software engineers now leading many LLM implementations rather than ML specialists - Lower barrier to entry has increased stakeholder involvement across organizations ### Evaluation Challenges - Traditional ML evaluation relied on: - LLM evaluation faces new challenges: ## Evaluation Framework Components ### Multiple Evaluation Layers - Outcomes-based evaluation - Proxy metrics - Public benchmarks ### Balancing Automated and Human Evaluation - Need for both automated systems and human feedback - Domain expert involvement crucial for specialized applications - Continuous feedback loop from end-users - Importance of stakeholder involvement throughout development ## Production Monitoring and Quality Assurance ### Experimental Setup Requirements - Stable prompt sets for benchmarking - Continuous monitoring systems - Custom observability dashboards - Regular sampling of system health - Alert systems for quality degradation ### Tools and Frameworks - NVIDIA Nemo Guardrails - Gantry's infrastructure layer ## Common Production Use Cases ### Primary Categories - Information Retrieval - Chat Applications - Text Generation ### Success Factors - Product context more important than technical implementation - Need to account for model fallibility - Design systems that gracefully handle errors - Focus on user outcomes rather than model metrics ## Best Practices for Deployment ### Data Quality and Preparation - Focus on data curation and quality - Iterative evaluation process - Regular checkpointing and logging - Continuous monitoring of performance ### Development Approach - Start with existing models before custom training - Use prompt engineering as first solution - Focus on product-market fit before model optimization - Build robust feedback loops ### Safety and Control - Implementation of guardrails - Topic adherence monitoring - Security considerations - Prevention of inappropriate content - Management of external application access ## Future Considerations ### Tooling Gaps - Need for better prompt engineering tools - Requirement for more sophisticated evaluation frameworks - Integration of human feedback systems - Standardization of evaluation metrics ### Industry Evolution - Moving from manual to automated evaluation - Development of domain-specific benchmarks - Integration of continuous monitoring systems - Balance between automation and human oversight - Focus on real-world application performance ## Impact on Development Process - Faster deployment cycles - More stakeholder involvement - Need for continuous evaluation - Importance of feedback loops - Focus on practical outcomes over technical metrics