LLM Evaluation
Definition
Evaluation of language models systematically measures quality, safety, cost, and robustness for defined tasks.
Public benchmarks are rarely sufficient. Good evaluations combine custom test cases, automatic metrics, human evaluation, and, if applicable, LLM-as-a-Judge.
Simply explained
How to build a test plan for a model instead of just looking at individual answers.
Why it matters in practice
Central for model selection, prompt changes, and productive quality assurance.
Example from business
An assistant is evaluated on 200 real anonymized tasks with correctness, source referencing, latency, and cost.
Limits and pitfalls
- Only use benchmarks; manually adjust test sets after changes; do not log model versions; do not analyze error classes.
Origin & history
Liang et al. introduced HELM in 2022 as a multidimensional evaluation approach for language models.
Use in mid-sized companies
- Possible application in SMEs: Define a fixed evaluation set with acceptance thresholds before pilot and deployment.
Frequently asked questions
Is a benchmark enough?
No. Should evaluations be versioned? Yes.
Sources
- HELM · Liang et al. (2022), HELM
- Ergänzender Primärbeleg · Ergänzender Primärbeleg
- Ergänzender Primärbeleg · Ergänzender Primärbeleg
Source verification: 10./12.10.2026.
Editorially reviewed on: