LLM Evaluation
Also known as: LLM eval, Evals
Measuring whether an LLM does what you want, beyond "looks fine to me".
The practice of measuring LLM quality across dimensions like accuracy, fluency, faithfulness, safety, and latency. Combines automated metrics (BLEU, ROUGE, BERTScore) with human evaluation and LLM-as-judge approaches.
In practice
The most under-prepared interview area. Be ready to design a real eval harness (golden set, LLM-as-judge, regression tracking) for a specific product.
Comparisons that include LLM Evaluation
Related topics
Related terms
Hallucination
When a model confidently makes up something that isn't true.
Perplexity
Exp(average cross-entropy) on held-out text; lower means the model is less surprised by real data.
Reasoning Model
An LLM trained to reason at length internally before answering. Slower and more expensive, but much better on hard problems.
Guardrails
Pre- and post-processing layers that block bad inputs/outputs and enforce policy on top of an LLM.
MMLU (Massive Multitask Language Understanding)
Multiple-choice benchmark across 57 academic subjects; the standard "raw knowledge" headline number.
HumanEval
Code-generation benchmark: 164 problems with hidden unit tests, scored by whether the generated code passes.