Evaluation
ELI5 — The Vibe Check
Evaluation in AI is figuring out if your model actually works — not just on test data, but in the real world. It's the 'does this thing actually do what we need?' phase. You test with benchmarks, human reviewers, automated metrics, and real user feedback. The hardest part? Defining what 'good' even means for your specific use case.
Real Talk
AI evaluation encompasses the methods and metrics used to assess model performance. It includes automated metrics (accuracy, F1, BLEU, perplexity), benchmark suites (MMLU, HumanEval), human evaluation (preference ratings, Elo scores), and domain-specific assessments. Modern LLM evaluation uses LLM-as-judge approaches, red teaming, and task-specific evaluation frameworks.
When You'll Hear This
"We need an eval suite before deploying the model." / "The benchmark scores look great but the human eval tells a different story."
Related Terms
Benchmark
In AI, a benchmark is a standardized test that measures how good a model is — like the SAT for AI.
F1 Score
The F1 Score is the balanced average of precision and recall — a single number that captures both.
LLM (Large Language Model)
An LLM is a humongous AI that read basically the entire internet and learned to predict what words come next, really really well.
Precision
Precision asks: 'Of all the times the AI said YES, how often was it actually right?
Recall
Recall asks: 'Of all the actual YES cases in the world, how many did the AI catch?' High recall means the model finds almost everything it should.