Golden Set
Also known as: Eval set, Regression set
Curated test cases with expected outputs; your regression suite for prompt and model changes.
A curated set of test cases with known-correct expected outputs, used as the ground-truth regression suite for an LLM app. Should cover common cases, edge cases, and known failure modes.
In practice
First thing to build before any LLM app launch. Interviews probe size, sourcing, drift handling.
Related topics
Questions that mention this term
- Design the golden set for a customer support chatbot that has to gate every prompt PR
- The model provider just updated the model behind the API endpoint. Describe the regression testing process before allowing traffic.
- Which of these belong on the ship checklist for a production fine-tune?
- Before shipping a prompt change, the lead says 'run it against the golden set.' What is a golden dataset and why does every team need one?
- Design the post-FT eval suite for a customer support fine-tune
- A production model has no ground truth labels on live traffic. Design a monitoring plan that catches degradation before users complain.
Related terms
Chatbot Arena
Crowdsourced blind A/B comparisons of LLM responses, aggregated into an Elo leaderboard.
Eval Harness
A framework for running, scoring, and tracking LLM evals over time; handles golden sets, judges, and regressions.
Guardrails
Pre- and post-processing layers that block bad inputs/outputs and enforce policy on top of an LLM.
Hallucination
When a model confidently makes up something that isn't true.
HumanEval
Code-generation benchmark: 164 problems with hidden unit tests, scored by whether the generated code passes.
Jailbreak
Adversarial prompt that bypasses an LLM's safety training to elicit normally-refused content.