Lion Cipher Lab
Measure whether an LLM is actually good (beyond vibes)
- AI Security
- Beginner
- ~45 min
- Free · no account required
Why it matters
A model that gives one impressive answer in a demo tells you almost nothing — the demo was chosen because it works. The people who use AI responsibly are the ones who can answer "how do you know it's good?" with a number and a method instead of a feeling, and that skill is evaluation. It is also, quietly, a security skill: the same discipline that measures whether a model is accurate is what measures whether it is safe — whether it refuses what it should and whether it can be talked out of those refusals. Learning to evaluate a model you own, on tasks you define, is the difference between…
What you'll learn
- evaluating LLMs
- running benchmarks
- lm-evaluation-harness
- Hugging Face evaluate
- building a task-specific eval
- safety and refusal testing
Steps in this lab
- Why a good demo proves nothing
- The four kinds of evaluation, named
- Set up your measuring tools
- Run a real benchmark on a small model you own
- Score outputs yourself with a metric library
- Build a tiny task-specific eval of your own
- The security axis: evaluate safety and refusal, not just accuracy
- What you can now say that most people can't