Name: Mastering LLM Evaluation: Build Reliable Scalable AI Systems
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What You'll Learn

Understand the full lifecycle of LLM evaluation—from 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

Requirements

No prior experience in evaluation required—this course starts with the fundamentals
Basic understanding of how large language models (LLMs) like GPT-4 or Claude work
Familiarity with prompt engineering or using AI APIs is helpful, but not required
Comfort reading JSON or working with simple scripts (Python or notebooks) is a plus
Access to a computer with internet connection (for labs and dashboards)
Curiosity about building safe, measurable, and cost-effective AI systems!

AI/ML engineers building or fine-tuning LLM applications and workflows
Product managers responsible for the performance, safety, and business impact of AI features
MLOps and infrastructure teams looking to implement evaluation pipelines and monitoring systems
Data scientists and analysts who need to conduct systematic error analysis or human-in-the-loop evaluation
Technical founders, consultants, or AI leads managing LLM deployments across organizations
Anyone curious about LLM performance evaluation, cost optimization, or risk mitigation in real-world AI systems

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