Evaluation

How to measure whether an AI system is getting better: eval sets, error analysis, LLM judges, the limits of public benchmarks and monitoring in production.

6 lessonsabout 65 min

Changing a prompt or a model without measuring the result is guessing. Model outputs vary from run to run, a fix for one kind of request can quietly break another, and a few hand-picked examples cannot show the difference. Evaluation is the skill that turns AI development from guessing into engineering.

You will start with why evals matter and the loop that uses them, then build an eval set from real traffic, read traces to find and count failure types, and use a model as a judge without trusting it blindly. You will see what public benchmarks can and cannot tell you, and finish with online evaluation: user feedback, A/B tests and watching for new kinds of input in production.

Lessons

AI-10.01 Why evals are the core skill Without evaluations, every prompt or model change is a guess. Beginner 10 min
AI-10.02 Building an eval set A good eval set reflects real usage: sample actual user inputs, add known hard cases and failure modes, and generate synthetic cases for gaps. Intermediate 12 min
AI-10.03 Error analysis Error analysis means reading real outputs and traces, writing down what went wrong in each failure, and grouping the notes into categories. Intermediate 10 min
AI-10.04 LLM-as-judge An LLM judge grades outputs using a rubric prompt, which scales evaluation beyond what humans can review. Intermediate 12 min
AI-10.05 Benchmarks and their limits Public benchmarks measure abilities such as knowledge, math, coding or reasoning on fixed question sets. Beginner 10 min
AI-10.06 Online evaluation and monitoring Offline evals do not catch everything, so production systems monitor real usage: user feedback, task completion, escalation rates, refusal rates and samples graded by judges or humans. Intermediate 10 min

Start with lesson 1