Definition
Evaluation compares predictions to ground truth or human judgments using metrics (F1, BLEU, perplexity, win rate). For LLMs, automated benchmarks plus human/LLM-as-judge evals are common.
Good evaluation matches the product risk — a toy accuracy score is not enough for safety-critical use.
In simple terms
Exams, road tests, and customer reviews — different ways to check readiness before you trust the system in the wild.
Where you see it
- MMLU-style knowledge benchmarks for LLMs.
- Offline F1 on a Somali NER test set.
- A/B tests on live user satisfaction.
How it works
1.Define success
Pick metrics tied to the real goal.
2.Build a test set
Clean, representative, leakage-free.
3.Measure and iterate
Compare models; watch for regressions.
Why it matters
- Without evaluation, you cannot tell improvement from wishful thinking.
Often confused
Leaderboard rank equals product quality.
Public benchmarks can be gamed or mismatched to your users and language.