LLM and RAG Evaluation Playbook for Production Apps

Learn to evaluate and monitor LLM and RAG applications for production deployment in this comprehensive 58-minute workshop. Discover the critical importance of quantifying system metrics before optimization, as building proof-of-concept applications is straightforward, but achieving production-ready performance requires systematic evaluation of accuracy, latency, costs, and reproducibility. Explore how to transform iterative AI development by implementing evaluation layers that clearly indicate improvement areas. Work with a predefined agentic RAG system built in LangGraph to understand practical evaluation techniques. Master adding prompt monitoring layers to track system behavior and visualize embedding quality to assess retrieval effectiveness. Evaluate retrieval context quality for RAG applications and compute application-level metrics that expose hallucinations, moderation issues, and performance using LLM-as-judges methodology. Learn to log metrics to prompt management tools for systematic experiment comparison and optimization tracking. Understand why evaluation serves as the foundation for all production optimization efforts, including fine-tuning specialized LLMs, optimizing inference performance, and ensuring compliance with production requirements. Gain practical skills for building simple end-to-end systems with integrated evaluation layers that enable rapid iteration in the right direction for production-ready AI applications.