LLM-as-a-judge
Using a language model to assess the outputs of another AI system against defined criteria, such as factual support, relevance, tone or policy compliance. It makes it practical to evaluate large volumes of generated text.
Why it matters
Human review of every output does not scale, and simple metrics cannot judge whether a summary is faithful or a reply is appropriate. A judge model can score outputs consistently and flag the ones that need a person’s attention.
Judges have their own biases and errors. Their scores should be calibrated against human-labelled examples, the criteria should be specific, and important decisions should not rest on a judge’s assessment alone.
In practice
For example, a UK bank piloting complaint-response drafting might use a judge model to check each draft for unsupported claims and missing regulatory wording, and compare its ratings monthly against a sample reviewed by the complaints quality team.
Where Rodan fits
Rodan builds evaluation pipelines, including calibrated judge models, for systems delivered through Applied AI Engineering.