Practice vocabulary for evaluating LLMs in production applications: eval suites, hallucination rate tracking, LLM-as-judge, golden datasets, and continuous evaluation.
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The team says: 'Our ___ suite runs on every prompt change in CI.' What is an eval suite?
An eval suite is an automated set of test cases — each with an input prompt and an expected output or quality criterion — that runs in CI to catch regressions in LLM behaviour whenever the prompt, model, or retrieval logic changes.
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The observability dashboard tracks ___ rate in production to monitor how often the model invents facts.
Hallucination rate measures how often the LLM produces outputs that contain invented, incorrect, or unsupported facts. Tracking it in production typically involves sampling outputs and having humans or a judge model label them as factual or hallucinated.
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The team uses an ___ judge to rate response quality at scale instead of relying solely on human raters.
An LLM judge (LLM-as-judge) is a separate LLM prompted to evaluate another LLM's outputs according to a rubric — rating relevance, accuracy, tone, and completeness. It scales human-like evaluation to millions of examples, though it introduces its own biases.
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Quality assurance is based on a ___ dataset of 500 hand-curated examples with verified correct answers.
A golden dataset is a manually curated set of examples where the correct answer or ideal output is known and verified. Evaluating against a golden dataset provides a reliable quality signal because the ground truth was established with care, not automatically generated.
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The engineering team runs ___ evaluation to catch quality regressions before each release.
Continuous evaluation means the eval suite runs automatically in CI/CD — triggered by pull requests, prompt changes, or model updates. It catches quality regressions before they reach users, analogous to automated testing in traditional software development.
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During a code review of the new prompt engineering pipeline, Sarah comments: 'I'm seeing inconsistent scores for this model across different evaluation frameworks. It seems we need to establish a more robust and automated ground truth.' What does 'ground truth' refer to in this context?
'Ground truth' in LLM evaluation signifies a reliable and objective reference point. It represents the established, verified standard against which the model's output is compared—crucially, it's not merely an opinion or cost; it's a dataset of confirmed correct answers. This concept is vital for quantifying and tracking model performance consistently.
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In a Slack channel dedicated to the project, Alex writes: 'We're running RAG evaluation metrics on this new retrieval strategy. The precision score is currently at 0.85 – that's pretty good, but we should monitor the recall closely as well.' What do 'precision' and 'recall' measure in this scenario?
'Precision' in RAG evaluation indicates how accurate the retrieved information is – it's the proportion of relevant documents among those retrieved. 'Recall,' conversely, measures how completely the retrieval system captures all relevant documents from the knowledge base. Understanding both is essential for optimizing the retrieval process.
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During a PR review, David describes the changes: 'We've integrated a new LLM evaluator that automatically generates response variations based on different prompt templates. This allows us to perform pairwise comparison evaluation and identify the most effective prompts.' What is 'pairwise comparison'?
'Pairwise comparison' is a key technique in LLM evaluation. It involves systematically comparing two responses at a time – this avoids bias introduced by ranking all responses simultaneously. Human raters (or automated systems) then determine which response is preferred or higher quality, providing valuable insights for prompt optimization.
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The team's documentation specifies that they use a 'gold standard dataset' to benchmark the LLM. What is the primary purpose of this dataset?
The 'gold standard dataset' is fundamentally a benchmark – it's a carefully constructed collection of correct answers or ideal outputs. This allows the team to objectively measure how well the LLM performs against a known and validated standard. It's crucial for tracking progress and identifying areas for improvement, rather than simply training data.
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During a standup meeting, the lead engineer says: 'We're running continuous evaluation on the model to catch quality regressions before each release. We're focusing on metrics like hallucination rate and coherence.' What do 'hallucination rate' and 'coherence' refer to in this context?
'Hallucination rate' identifies instances where the LLM generates false or misleading information – it's a measure of factual inaccuracy. 'Coherence,' on the other hand, assesses whether the model's response is logically consistent and makes sense in context. Monitoring both metrics provides a comprehensive understanding of the LLM's quality.
What will I practise in "LLM Evaluation in Applications Vocabulary"?
Practice vocabulary for evaluating LLMs in production applications: eval suites, hallucination rate tracking, LLM-as-judge, golden datasets, and continuous evaluation.
How many exercises are in this module?
This module has 10 multiple-choice exercises, each with instant feedback and a full explanation of the correct answer.
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Where can I find more LLM App Development exercises?
Browse the full LLM App Development hub for related drills, or check the "Next up" link below to continue with a connected topic.
How is this different from reading an article on the same topic?
Articles explain vocabulary and concepts in prose; this exercise tests and reinforces that vocabulary through active recall with immediate feedback — the two work best together.
Who writes these exercises?
Every exercise is written by the CoderSlingo team, drawing on real workplace English used in IT roles, then reviewed for accuracy and clarity.