Human Evaluation
Definition
Human evaluation assesses AI outputs by people according to defined criteria.
People judge, for example, correctness, usefulness, style, safety, or preferences. Good evaluation requires clear rubrics, suitable samples, and as consistent rating processes as possible.
Simply explained
Like a quality check by real reviewers instead of only automatic metrics.
Why it matters in practice
Highly relevant for open language and creative tasks where automatic metrics do not reliably capture important quality aspects.
Example from business
Professionals evaluate a sample of support responses for accuracy, completeness, tone, and escalation needs.
Limits and pitfalls
- Unclear criteria; sample size too small; raters not calibrated; unnecessarily showing sensitive data; treating subjective preference as objective truth.
Origin & history
Research on natural language generation documents methodological recommendations for human text evaluation, such as clearly defined criteria, suitable samples, and multiple raters (van der Lee et al., 2019).
Use in mid-sized companies
- Possible applications: acceptance of AI assistants, quality spot checks, comparison of prompts/models, and ongoing production monitoring.
Frequently asked questions
Is human evaluation objective?
Not completely; good rubrics and multiple raters can increase reliability. Does it replace automatic tests? No, both complement each other.
Sources
- Best practices for the human evaluation of automatically generated text · Van der Lee et al. (2019), Best practices for the human evaluation of automatically generated text
Source checked: 11.10.2026.
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