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

A logistics company uses a generative AI model to draft incident reports from sensor logs. Reviewers find the reports are often incomplete, missing fields such as root cause and corrective action. The team wants to improve output completeness without retraining the model. Which TWO techniques should they use? (Choose two.)

⚠ Common exam trap

The trap here is assuming that more creative sampling or longer/shorter outputs improve report quality, when completeness specifically requires an explicit field schema plus examples of fully populated reports.

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 few-shot examples of complete incident reports that show every required field filled in.

Completeness improves when the required output shape is made explicit and demonstrated. A structured schema enumerates every field so omissions become visible and correctable, while few-shot examples of complete reports show the model the expected coverage and detail. Sampling and length controls do not enforce required fields and can reduce or distort content.

Answer analysis

Option-by-option breakdown

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

✗
Shorten the input sensor logs to only the most recent readings.
Why it's wrong here
Trimming input reduces the evidence available for root-cause analysis and can cause more omissions, not fewer. Completeness depends on having sufficient source data plus a clear output specification. Discarding earlier sensor context removes signals that might explain an incident, so this technique works against the stated goal.
✓
Add few-shot examples of complete incident reports that show every required field filled in.
Why this is correct
Complete exemplars demonstrate the expected coverage pattern, teaching the model which sections must appear and how detailed they should be. Combined with a schema, examples reinforce the habit of filling all fields. This is a prompt-level change that improves completeness immediately without weight updates or new training data collection.
✗
Increase the model's temperature to encourage the model to explore more content.
Why it's wrong here
Higher temperature increases randomness and can add unsupported detail, but it does not systematically fill missing required fields. Incident reports need consistent coverage, not creative variation. Raising temperature risks fabricating root causes, which is worse than omission in a safety context, so it is not an appropriate completeness technique.
✗
Reduce the maximum output tokens to force the model to be concise.
Why it's wrong here
A tighter token cap pressures the model to drop sections, which directly worsens completeness. Incident reports need multiple required fields, and truncation removes the least salient content first. If any field is missing or underdeveloped, a lower cap makes that more likely, so it is the opposite of the desired improvement.
✓
Define a structured output schema listing every required field and instruct the model to populate each one.
Why this is correct
A structured schema enumerates the required fields, so the model treats each as a slot to fill rather than optional content. This directly addresses incompleteness and makes omissions detectable programmatically. Because it is prompt-level and format-level, it requires no retraining and integrates cleanly with downstream validation of incident reports.

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

Why each option matters

Option-by-option breakdown

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