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.

Intermediate

14 Lessons

Updated 1 week ago

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

23 Playgrounds

This course includes

23 Playgrounds

Course Overview

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...Show More

TAKEAWAY SKILLS

Generative Ai

Large Language Models (llms)

Testing

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

What You'll Learn

Understanding of systematic LLM evaluation and the critical role of traces and error analysis

Course Content

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.

What Kinds of LLM Evaluations Should You Run

Traces and Error Analysis Explained

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.

How to Capture and Review LLM Traces for Reliable Evaluation

Generating Synthetic Data for Evaluation and Edge-Case Testing Why Pass/Fail Beats Numeric Scales

Scaling Evaluation Beyond the Basics

Learn how to design evaluations that avoid misleading metrics, treat prompts as versioned system artifacts, and separate guardrails from evaluators.

Evaluating Real Systems in Production

Learn how to evaluate full conversations, turn recurring failures into reproducible fixes, and debug RAG systems using four simple checks.

How to Evaluate Multi-Turn LLM Conversations How to Evaluate Agentic Workflows Explaining Every RAG Failure

Wrap Up

Learn how to make evaluation an ongoing practice, use metrics wisely, and keep your AI system reliable as it scales.

Evaluation as a Core Part of Development How Formula-Based Metrics Fit into LLM Evaluation Conclusion

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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