Practice the vocabulary of scoring a model release against a repeatable evaluation suite.
0 / 5 completed
1 / 5
At standup, a dev mentions running a fixed set of representative prompts through a model release and scoring its outputs against a rubric before shipping it to users. What is this practice called?
LLM evaluation, or eval, benchmarking runs a fixed set of representative prompts through a model release and scores its outputs against a defined rubric before that release reaches users. Shipping a release directly with no scoring risks a quality regression going unnoticed until users encounter it. This benchmarking step is what turns 'the new model seems fine' into a measurable, repeatable comparison against the previous version.
2 / 5
During a design review, the team wants their eval suite to include a held-out set of prompts the model was never trained or tuned on, so the score reflects genuine generalization. Which capability supports this?
A held-out, uncontaminated evaluation set keeps its prompts entirely separate from anything the model was trained or tuned on, so a resulting score reflects genuine generalization rather than the model simply recalling an answer it already saw during training. Reusing training-adjacent prompts as the evaluation set inflates the score without reflecting real-world performance. This separation is essential for an eval score to mean anything trustworthy.
3 / 5
In a code review, a dev notices the eval pipeline uses an independent grading model, or a human rater, to score a free-form response's quality rather than only checking for an exact string match. What does this represent?
LLM-as-judge or human-rated scoring uses an independent grading model or a human rater to judge a free-form response's actual quality, which handles the reality that many good answers won't match one single fixed expected string exactly. Scoring only by exact string match penalizes a correct answer phrased differently than expected. This more flexible scoring approach is necessary for evaluating a model's open-ended, conversational output rather than a narrow, single-answer task.
4 / 5
An incident report shows a new model version scored well on the team's eval suite but performed noticeably worse in production on a category of query the eval suite didn't actually cover. What practice would prevent this?
Continuously expanding the eval suite's prompt coverage to reflect real production query patterns catches a category of query the original suite never tested against. Treating a fixed suite as permanently sufficient ignores that real usage patterns shift over time and can reveal a gap the original benchmark missed. This ongoing coverage review is what keeps an eval score a reliable predictor of actual production quality rather than a stale snapshot.
5 / 5
During a PR review, a teammate asks why the team runs a formal eval benchmark on every model release instead of just having a few engineers try some prompts manually and judge whether it seems fine. What is the reasoning?
A few engineers trying some prompts manually covers only a small, ad hoc slice of real usage and can easily miss a regression outside what they happened to test. A formal, repeatable eval benchmark covers a broader, consistent set of representative cases and produces a comparable score across releases. The tradeoff is the added upfront effort of building and maintaining a genuinely representative, uncontaminated eval suite.