NCA-GENL Experimentation Practice Question
An AI researcher is designing an experiment to compare two prompt templates for a customer-support LLM using NVIDIA NeMo. To ensure the comparison is fair and reproducible, which two practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is assuming that changing multiple factors at once—such as fine-tuning per template or varying temperature—provides a richer comparison, when it actually destroys the ability to attribute results to the prompt.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the same underlying LLM checkpoint and decoding parameters for both prompt templates.
A fair prompt-template comparison requires isolating the template as the only independent variable. Keeping the model checkpoint, decoding parameters, evaluation dataset, and scoring criteria identical across both conditions ensures that observed differences are caused by the prompt itself. This controlled design is the foundation of reproducible LLM experimentation and supports confident deployment decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vary the temperature for each template to explore a wider range of outputs.
Why it's wrong here
Varying temperature alongside the prompt template introduces a second independent variable. Differences in output quality could then be due to sampling randomness rather than the prompt. To isolate the template effect, decoding parameters such as temperature and top-p must remain fixed across conditions.
- ✗
Fine-tune the LLM separately for each prompt template before comparing them.
Why it's wrong here
Fine-tuning separately for each template changes the model weights, so any performance difference could come from the fine-tuning run rather than the prompt. This confounds the experiment. If fine-tuning is part of the study, it should be a separate factor, not mixed into a prompt-template comparison.
- ✗
Use a different evaluation metric for each template to capture their unique strengths.
Why it's wrong here
Different metrics for each template make the results incomparable. A fair experiment applies the same metric to both conditions. If multiple metrics are of interest, they should be computed for both templates. Using different metrics would bias the comparison and prevent a valid conclusion about which prompt performs better.
- ✓
Use the same underlying LLM checkpoint and decoding parameters for both prompt templates.
Why this is correct
Holding the model checkpoint and decoding parameters constant ensures that any difference in output quality is attributable to the prompt template rather than to model weights or sampling settings. This is essential for a controlled comparison, because changing the checkpoint or temperature would introduce confounding variables that make the results uninterpretable.
- ✓
Evaluate both prompt templates on the same held-out set of customer queries with identical scoring criteria.
Why this is correct
Using the same evaluation set and scoring rubric for both templates guarantees that performance differences reflect the prompts, not variations in question difficulty or grading. This controlled evaluation is fundamental to reproducibility and fairness, and it allows the team to make a defensible decision about which template to deploy.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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