Master the art and science of LLM evaluation with hands-on labs, error analysis, and cost-optimized strategies.
What you will learn
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Understand the full lifecycle of LLM evaluationβfrom prototyping to production monitoring
Identify and categorize common failure modes in large language model outputs
Design and implement structured error analysis and annotation workflows
Build automated evaluation pipelines using code-based and LLM-judge metrics
Evaluate architecture-specific systems like RAG, multi-turn agents, and multi-modal models
Set up continuous monitoring dashboards with trace data, alerts, and CI/CD gates
Optimize model usage and cost with intelligent routing, fallback logic, and caching
Deploy human-in-the-loop review systems for ongoing feedback and quality control
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