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2h
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LLM Evaluation: Building Reliable AI Systems at Scale
Learn to capture traces, generate synthetic data, evaluate agents and RAG systems, and build production-ready testing workflows so your LLM apps stay reliable and scalable.
This course provides a roadmap for building reliable, production-ready LLM systems through rigorous evaluation. You’ll start by learning why systematic evaluation matters and how to use traces and error analysis to understand model behavior.
You’ll build an evaluation workflow by capturing real failures and generating synthetic data for edge cases. You’ll avoid traps like misleading similarity metrics and learn why simple binary evaluations often beat complex numeric scales. You’ll also cover architectural best practices, including where prompts fit and how to keep guardrails separate from evaluators.
Next, you’ll evaluate complex systems in production: scoring multi-turn conversations, validating agent workflows, and diagnosing common RAG failure modes. You’ll also learn how tools like LangSmith work internally, including what they measure and how they compute scores. By the end, you’ll integrate evaluation into development with CI checks and regression tests to keep AI stable as usage and complexity grow.
This course provides a roadmap for building reliable, production-ready LLM systems through rigorous evaluation. You’ll start by ...Show More
WHAT YOU'LL LEARN
Understanding of systematic LLM evaluation and the critical role of traces and error analysis
Hands-on experience capturing and reviewing complete traces to identify system failures
Proficiency in generating structured synthetic data for edge-case testing and diverse behavior analysis
The ability to design binary pass/fail evaluations that outperform misleading numeric scales
The ability to manage prompts as versioned system artifacts within an evaluated architecture
Working knowledge of specialized evaluation for multi-turn conversations and agentic workflows
Understanding of systematic LLM evaluation and the critical role of traces and error analysis
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TAKEAWAY SKILLS
Learning Roadmap
1.
Foundations of AI Evaluation
Foundations of AI Evaluation
Learn why impressive demos fail without systematic evaluation, and how traces and error analysis form the foundation of building reliable LLM systems.
2.
Building the Evaluation Workflow
Building the Evaluation Workflow
Learn how to capture complete traces, generate structured synthetic data to expose diverse behaviors, and turn real failures into focused evaluations.
3.
Scaling Evaluation Beyond the Basics
Scaling Evaluation Beyond the Basics
3 Lessons
3 Lessons
Learn how to design evaluations that avoid misleading metrics, treat prompts as versioned system artifacts, and separate guardrails from evaluators.
4.
Evaluating Real Systems in Production
Evaluating Real Systems in Production
3 Lessons
3 Lessons
Learn how to evaluate full conversations, turn recurring failures into reproducible fixes, and debug RAG systems using four simple checks.
5.
Wrap Up
Wrap Up
3 Lessons
3 Lessons
Learn how to make evaluation an ongoing practice, use metrics wisely, and keep your AI system reliable as it scales.
Certificate of Completion
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