Master LLM Evaluation
For engineers who need to prove their AI actually works.
Shipping an LLM feature is easy; knowing whether it is good is the hard part. Build the vocabulary of evals, design a judge rubric, implement a metric by hand, then evaluate agents and a real production system.
- Speak precisely about offline vs online evals, judges and rubrics
- Design an LLM-as-judge rubric that resists gaming
- Implement eval metrics like token-level F1 from scratch
- Evaluate agents and conversational systems, not just single calls
51 LLM Evals Interview Questions
The vocabulary of evals — start here.
LLM-as-Judge Rubric Builder
Design an LLM-as-judge rubric interactively.
Token-Level F1
Implement a real metric by hand, with tests.
The Agent Evaluations Handbook
Evaluate agents — multi-step, tool-using.
Design an AI Agent System
The agent system your evals must judge.
Design LLM Eval & Observability
Build the eval + observability system around production.
The Senior AI Engineer Interview Handbook
Tie evals into senior-scope AI engineering.
Why evaluation is the hard part
Anyone can get an LLM feature to work once; proving it works reliably is what's hard — and what this path teaches. You'll build the vocabulary of evals (offline vs online, judges, rubrics), design an LLM-as-a-judge rubric that resists gaming, implement a real metric like token-level F1 by hand, then evaluate agents and a full production system rather than single calls.
The recurring lesson: a number you haven't validated against human judgement can mislead you with total confidence. This path is how you earn the right to trust your metrics — the skill that lets you actually improve an AI system instead of guessing.