LLM Evals · Turn quality into a signal
What makes a good golden dataset?
Short answer
Representative real inputs, clear expected behaviour, meaningful edge cases, consistent labels and version control. It should reflect the product's decisions, not just easy examples.
A golden dataset is the reference against which every change is judged. If it is unrepresentative, the score is misleading. If the labels are ambiguous, engineers optimise noise. Quality beats size: a focused set of well-labelled cases is more useful than thousands of unlabelled rows.
- Sample from real user queries across intents and user types.
- Include known failures, edge cases and out-of-scope requests.
- Define expected behaviour before seeing model output.
- Version the set and record why each case exists.
- Measure label agreement and resolve ambiguous cases.
Follow-ups you should expect
Interviewers use these to check whether the first answer was understanding or memorisation.
- 1How would you handle a case with two valid answers?
- 2When should you retire an eval case?
- 3How do you prevent overfitting to the dataset?
Say it in the interview
A good golden set looks like real usage, not like a demo. I sample actual queries, add edge cases and past failures, and write expected behaviour before seeing what the model does. Labels have to be clear enough that two reviewers agree. I version it and grow it from production incidents. A smaller set of sharp cases is more useful than a huge pile of ambiguous ones.
Use this as a model, not a script. Saying it in your own words is what makes it survive follow-up pressure.
Related questions
Reading is step one
Now say it out loud.
Understanding an answer and delivering it under pressure are different skills. Rehearse this topic in a live LLM Evals mock interview and get scored feedback on depth, clarity and confidence.