AI0-001 AI Concepts and Techniques Practice Question
An AI engineer is tuning a large language model for a summarization task. The output summaries are too verbose and include irrelevant details. Which technique should be applied to encourage concise outputs?
⚠ Common exam trap
CompTIA often tests the misconception that adjusting sampling parameters (top-k, temperature) is the primary way to control output length, when in fact these parameters affect randomness and diversity, not the explicit length or relevance of the generated text.
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
✓
Provide a few-shot example with concise summaries
Providing a few-shot example with concise summaries (Option A) directly demonstrates the desired output format to the model, leveraging in-context learning to bias generation toward brevity and relevance. This is the most effective technique for controlling output style without altering the model's underlying parameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Provide a few-shot example with concise summaries
Why this is correct
Few-shot prompting supplies in-context demonstrations that steer the model's output distribution toward the desired style. By including concise summary exemplars in the prompt, the model infers the expected length and level of detail, directly countering the verbosity and irrelevant content described in the stem without retraining.
- ✗
Use chain-of-thought prompting
Why it's wrong here
Chain-of-thought prompting elicits intermediate reasoning steps, which lengthens output and adds detail rather than suppressing it. It is tempting because it improves accuracy on multi-step reasoning tasks, and it would be the right choice where the model must solve a problem, not compress text.
- ✗
Decrease the top-k value
Why it's wrong here
Lowering top-k narrows sampling to fewer high-probability tokens, which curbs randomness but not length; verbosity stems from the prompt or max-token limit. Top-k is for controlling output diversity in creative generation, where restricting token choice prevents incoherent tangents.
- ✗
Increase the temperature
Why it's wrong here
Raising temperature increases sampling randomness, producing more varied and often more tangential wording, not concision. It is tempting because it is a common knob for controlling generation style, and it would be correct where diverse or creative outputs are wanted rather than tightly focused summaries.
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Written by Johnson Ajibi, MSc IT Security
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