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Evaluating & Testing LLM Applications

Go beyond basic metrics. Rigorously test your LLM applications for reliability, safety, and real-world performance.

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(1,255)
1,255 studentsEnglish

Created by Dr. Priya Nair · AI Educator & Former ML Engineer

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$789$1,149Save 31%
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This course includes

  • 15 video lessons
  • 4h 14m of content
  • Downloadable resources
  • Full lifetime access
  • Certificate of completion
  • 30-day money-back guarantee

What you'll learn

  • Define concrete evaluation metrics for LLM applications.
  • Implement automated testing strategies for common LLM failure modes.
  • Design and execute effective human-in-the-loop evaluation protocols.
  • Identify and mitigate risks related to bias, toxicity, and hallucination.
  • Utilize frameworks like RAGAS and DeepEval for quantitative assessment.
  • Conduct adversarial testing to uncover edge case vulnerabilities.
  • Benchmark LLM application performance against established baselines.
  • Establish a continuous evaluation pipeline for deployed applications.

About this course

Building with Large Language Models (LLMs) isn't just about getting them to generate text; it's about ensuring those applications are robust, safe, and actually useful. This course dives deep into the methodologies and tools you need to critically assess your LLM-powered products before they hit your users. We'll cover everything from establishing clear evaluation criteria to implementing automated testing frameworks and conducting essential human-in-the-loop validation. You'll learn to identify subtle failure modes, measure bias, and ensure your application behaves predictably under diverse conditions. Stop guessing and start validating. This advanced program equips you with the practical skills to build trust and confidence in your LLM applications, transforming them from interesting experiments into reliable tools.

Course content

5 modules · 15 lessons · 4h 14m

  • Why Standard Software Testing Isn't EnoughPreview
    18m
  • Defining Your Evaluation Scope and MetricsPreview
    22m
  • Setting Up Your Evaluation Environment
    15m

Requirements

  • Solid understanding of LLM fundamentals and common architectures.
  • Experience with Python programming and relevant libraries (e.g., LangChain, Transformers).
  • Familiarity with software testing principles.

Your instructor

D

Dr. Priya Nair

AI Educator & Former ML Engineer

Priya has trained 40,000+ students and led ML teams at two startups. She specialises in making complex AI approachable.