LLM Evaluation & Benchmarking Course

Learn datasets, scoring, confidence intervals, benchmark limitations, LLM judges, and release decisions through 18 lessons and interactive experiments.

Foundations of evaluation

Build your intuition. What are we actually measuring?

• What is an LLM evaluation? — An impressive answer is an observation. An evaluation turns many observations into evidence for a decision.
• Build a representative dataset — What you choose to test determines what you are able to discover.
• Baselines, controls & reproducibility — A new score becomes useful when you can explain what changed and what it improved upon.

Metrics that mean something

Go beyond accuracy. Choose the right signal for the task.

• Accuracy, precision, recall & F1 — The same predictions can look excellent or terrible depending on the question your metric asks.
• Scoring open-ended generation — There can be many good answers to a question, and a fluent answer can still be wrong.
• Confidence is not correctness — A model that knows when it might be wrong can be more useful than one that is always certain.

The science behind the score

Understand uncertainty, significance, and fair comparisons.

• Sample size & confidence intervals — 82% on 50 examples and 82% on 5,000 examples are very different amounts of evidence.
• Compare systems on the same examples — The most informative comparison asks where two systems disagree.
• Avoid misleading experiments — If you try enough ideas, one can look like a breakthrough just by chance.

Navigate the benchmark landscape

Learn what public benchmarks can—and cannot—tell you.

• MMLU, GSM8K, HumanEval & beyond — A benchmark is a lens on a capability, not a universal intelligence score.
• Contamination, saturation & leakage — A system that has seen the answers may look capable without demonstrating generalization.
• Quality, latency & cost tradeoffs — The best system is often the one that satisfies the task at an acceptable operating cost.

Evaluate real-world LLM systems

Test judges, retrieval, and agents with confidence.

• Build and validate an LLM judge — A judge is another measurement instrument. It needs its own evaluation.
• Separate retrieval from answer quality — When a grounded assistant fails, locate the failure before changing the prompt.
• Evaluate agents & tool use — For an agent, the final text is only one part of the behavior you need to measure.

From experiment to production

Design your eval suite and turn evidence into decisions.

• Design an evaluation suite — A useful evaluation suite connects product risks to repeatable tests and explicit decisions.
• Online evaluation & drift — Passing an offline test is the beginning of measurement, not the end.
• Capstone: write a decision-ready eval plan — Bring the pieces together. Build a plan another person could run and use to make the same decision.