Intermediate Free 120 min total Course 07 of 90

Evaluating
AI Models

A model scores 92% on an internal benchmark. Three months after deployment it gives a patient incorrect medication dosing information. The score was real. The evaluation was wrong. This course teaches you how to evaluate AI systems so that what you test is what actually matters in the real world.

Start Module 1 → Talk to Arjun
Is this course for you?
This IS for you if
  • You are building or deploying AI models and want to know if they actually work
  • You have seen benchmark scores that did not match real-world performance
  • You need to select between AI vendors or models and want a structured comparison framework
  • You are responsible for AI quality, safety, or compliance at your organization
  • You work in a specialized domain like healthcare, finance, or law where general benchmarks fall short
This is NOT for you if
  • You want a deep statistical treatment of measurement theory (this course is practical)
  • You are looking only for evaluation of image or audio models (focus is text and language models)
  • You already have a mature evaluation suite with automated CI, red-team cadence, and domain experts in place
ⓘ
Prerequisite: How LLMs Work (Course 01). You should know what a language model is, what training and inference mean, and what a token is. If those words are unfamiliar, start with the Model Development Fundamentals course first.
What you will be able to do
✓Explain why benchmark scores routinely overestimate real-world performance and name three structural reasons why
✓Select the right benchmark for a given use case and explain what each major benchmark measures and misses
✓Design a human evaluation study with proper annotator selection, rating scale, and inter-annotator agreement measurement
✓Build a domain-specific evaluation dataset using the MEDFIT-LLM framework and calibrate confidence against accuracy
✓Run a structured red-team session to find safety failure modes before deployment
✓Set up a continuous evaluation pipeline with regression testing, drift alerting, and a feedback flywheel

The Six Modules

01
Why Evaluation Matters
Why benchmark scores lie: distribution shift, test-set contamination, Goodhart's Law. Intrinsic vs extrinsic evaluation. The evaluation lifecycle: pre-deployment, post-deployment, continuous.
20 min
→ 02
Benchmarks and Standard Metrics
MMLU, BIG-bench, TruthfulQA, MT-Bench, and HELM. What each measures, what it misses, and how to spot contaminated benchmarks. BLEU and ROUGE for generation tasks.
20 min
→ 03
Human Evaluation
Why automated metrics miss what humans notice. Designing a human evaluation study: task design, annotator selection, rating scale. Cohen's kappa for inter-annotator agreement. The Chatbot Arena methodology.
20 min
→ 04
Domain-Specific Evaluation
Why general benchmarks fail in specialized domains. The MEDFIT-LLM framework for healthcare AI. Building a domain evaluation set. Calibration: matching confidence to accuracy. Evaluating structured outputs.
20 min
→ 05
Red-Teaming and Safety Evaluation
Manual vs automated red-teaming. Common attack vectors: prompt injection, jailbreaks, indirect injection. Perez et al. automated red-teaming. Writing a red-team report with severity ratings and mitigations.
20 min
→ 06
Continuous Evaluation
Why models drift without retraining. Regression test suites with golden sets and alerting. Monitoring output distribution, faithfulness drift in RAG systems. The evaluation flywheel: errors feed datasets.
20 min
→

Supporting Blog Series

Six articles on AI evaluation for practitioners and technical leaders. Read any one independently or work through them alongside the course modules.

01 Why AI evaluation matters 02 Understanding AI benchmarks 03 Human evaluation of AI systems 04 Domain-specific AI evaluation 05 Red-teaming AI models 06 Continuous AI evaluation

Related Courses

Executive
AI for Enterprise Leaders
Intermediate
Retrieval-Augmented Generation
All Courses
AJ University: 90 Free AI Courses

Ready? Start with Module 1.

No account. No paywall. No credit card. Just start learning.

Begin Module 1 →