Techniques to Improve Generative AI Model Output →hardMultiple Select

Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output

A healthcare analytics team uses a Gemini model to generate patient-friendly discharge summaries from clinical notes. Clinicians report that summaries occasionally omit critical follow-up instructions. The team must improve recall of these instructions without retraining the model. Which two techniques should they apply? (Choose two.)

⚠ Common exam trap

The trap here is equating deterministic or lower-variance decoding with completeness, when consistency and recall are separate properties of a generative system.

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

✓

Add an explicit instruction to extract all follow-up actions into a mandatory checklist section of the summary.

Few-shot examples and an explicit mandatory checklist section both shape the model toward systematically extracting follow-up actions, improving recall without retraining. Temperature changes, token limits, and deterministic decoding affect randomness or length, not whether required clinical content is captured in the output.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Reduce the maximum output tokens to force the model to be more concise and include only essentials.
Why it's wrong here
Truncating the output budget can cut off the summary mid-sentence or pressure the model to drop sections, which directly worsens omission of follow-up instructions. Length limits are a formatting concern; they do not improve recall of clinically important content and may cause silent loss of required sections.
✗
Increase the temperature so the model produces more varied summaries and captures more details.
Why it's wrong here
Higher temperature increases sampling randomness, which makes output less consistent and more likely to omit or rephrase required content. Recall of a fixed set of instructions depends on structured extraction, not on creative variation, so raising temperature works against the goal and complicates clinical review.
✗
Enable deterministic decoding and assume the model will then always include every instruction.
Why it's wrong here
Deterministic decoding reduces run-to-run variance but does not guarantee completeness. A deterministic model can consistently omit the same instruction if the prompt never asked for it. Determinism makes behavior reproducible, which helps testing, but recall must be driven by explicit extraction instructions or examples.
✓
Add an explicit instruction to extract all follow-up actions into a mandatory checklist section of the summary.
Why this is correct
A direct, unambiguous instruction to place every follow-up action in a required section gives the model a clear structural target and reduces the chance that an item is dropped during free-form generation. Combined with a defined output schema, it makes omissions visible and easier to catch during review.
✓
Use few-shot prompting with examples that demonstrate surfacing every follow-up instruction in a dedicated section.
Why this is correct
Few-shot examples show the model the exact pattern of extracting and listing follow-up instructions in a consistent section. Because the pattern is demonstrated rather than merely described, the model is far more likely to reproduce the behavior on new notes, improving recall of critical items without any weight updates or training infrastructure.

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Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output

Why each option matters

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