Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A financial services firm uses a Gemini model to generate quarterly risk summaries from internal reports. Reviewers note that summaries sometimes contradict the source tables. The team wants the model to reason step by step over the figures before writing the summary. Which technique should they use?
⚠ Common exam trap
The trap here is treating determinism or brevity as a cure for factual inconsistency, when neither supplies the step-by-step reconciliation the task requires.
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 chain-of-thought prompting by instructing the model to compute and list intermediate values before producing the final summary.
Chain-of-thought prompting makes the model compute and compare intermediate values before writing, which is exactly what is needed to keep a summary consistent with source tables. Sampling changes and output limits affect randomness or length but not numerical reasoning. When a task requires multi-step reconciliation, prompting for explicit steps is the appropriate technique.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable a higher top-p value so the model samples more broadly and can reconcile conflicting numbers.
Why it's wrong here
Top-p controls the cumulative probability mass considered during sampling and does not add arithmetic or reconciliation ability. Broadening it increases variety and can introduce more inconsistency, not less. Reasoning over figures requires a deliberate method, not a sampling adjustment, so this choice does not resolve contradictions with source tables.
- ✗
Set temperature to zero so the model always produces the same summary and cannot contradict the tables.
Why it's wrong here
Zero temperature makes output deterministic but does not guarantee correctness; the same wrong reconciliation can be produced every time. It offers no mechanism for checking figures. Determinism is useful for reproducibility, yet it does not supply the multi-step reasoning needed to align narrative text with tabular data.
- ✓
Use chain-of-thought prompting by instructing the model to compute and list intermediate values before producing the final summary.
Why this is correct
Chain-of-thought prompting directs the model to work through intermediate calculations and comparisons before drafting the summary, which reduces contradictions because the figures are reconciled first. It makes the reasoning inspectable, so reviewers can spot errors. This technique is well suited to tasks that require multi-step arithmetic and consistency checking.
- ✗
Lower the maximum output tokens so the model is forced to be concise and avoid contradictory statements.
Why it's wrong here
Truncating output does not improve numerical consistency; it can cut off the summary mid-sentence and hide errors. Brevity is not accuracy. The model still lacks a step-by-step reconciliation process, so limiting length does nothing to align the narrative with the source tables.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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