Comparison
Ground Truth vs Reference-Based Evaluation
Ground Truth and Reference-Based Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Ground Truth
Ground Truth comes up when the question is fundamentally about evaluation.
For a coding eval: ground truth = the passing tests in the repo at HEAD.
When you would reach for Reference-Based Evaluation
When ground truth is available and one or a small number of outputs are clearly correct — extraction, classification, structured-output, code-with-tests.
A classifier eval: gold label is "spam", model output is "spam" → match, score 1.
Frequently asked
What is the difference between Ground Truth and Reference-Based Evaluation?
Ground Truth: Ground truth is the known-correct answer for an eval input. For supervised tasks it is the label used to grade model outputs; for LLM apps it is often human-curated reference answers. Reference-Based Evaluation: Reference-based evaluation compares the model output against a known correct answer using exact match, edit distance, BLEU, ROUGE, or LLM-as-judge "matches the reference."
When should I use Ground Truth vs Reference-Based Evaluation?
Ground Truth is the right concept when you are focused on evaluation. When ground truth is available and one or a small number of outputs are clearly correct — extraction, classification, structured-output, code-with-tests.
Are Ground Truth and Reference-Based Evaluation the same thing?
No. Ground Truth is evaluation; Reference-Based Evaluation is evaluation. They are related but address different parts of the AI stack.