Learn how to bootstrap evaluation with synthetic data, annotate and categorize LLM failures, and build a systematic error tracking system to prioritize issues based on business impact.

Focus on designing rule-based and LLM-judge metrics, building versioned evaluation datasets, and implementing scripted, end-to-end evaluation pipelines.

Establish team-based evaluation workflows, calculate inter-rater agreement using Kappa metrics, and run alignment workshops to build team consensus on model scoring.

Deep dive into specialized evaluations for Retrieval-Augmented Generation (RAG) systems, multi-step agent pipelines, multi-modal systems, and multi-turn conversational agents.

Implement tracing and observability workflows, configure automated CI/CD evaluation gates, and design safe guardrails for continuous deployment.

Design strategic sampling pipelines, optimize reviewer interfaces, and establish continuous user feedback loops for production systems.

Analyze LLM cost efficiency, implement smart routing rules between models of different tiers, and establish prompt caching mechanisms to minimize token spend.

Demonstrate your understanding of LLM evaluation by applying metrics, error analysis, observability, human review, and cost optimization principles to practical scenarios.