Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A product team uses a translation model to convert English product descriptions into French. The model mixes formal and informal French dialects. Which simple prompt modification likely solves this?
⚠ Common exam trap
Google often tests the misconception that fine-tuning or few-shot examples are always necessary for style control, when in fact a system prompt is the simplest and most scalable solution for inference-time behavior modification.
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 a system prompt specifying 'Use only formal French with no informal expressions.'
Adding a system prompt that explicitly instructs the model to 'Use only formal French with no informal expressions' directly constrains the output style at inference time without requiring retraining. This leverages the model's instruction-following capability to enforce a specific dialect, which is the simplest and most effective modification for controlling output style in a production translation pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature to encourage more consistent output.
Why it's wrong here
Raising temperature increases sampling randomness, which widens variation between formal and informal French rather than resolving it. It is tempting because temperature is the standard knob for output diversity, and would be correct if the goal were more varied phrasing instead of consistent register.
- ✓
Add a system prompt specifying 'Use only formal French with no informal expressions.'
Why this is correct
A system prompt constrains the model's output style before user input is processed, forcing formal French register and suppressing informal vocabulary. This directly addresses the mixed-dialect problem without retraining, satisfying the requirement for a simple prompt-level modification.
- ✗
Fine-tune the model on a corpus of formal French texts.
Why it's wrong here
Fine-tuning changes model weights through training and is neither a prompt modification nor immediate, and it exceeds what the question asks for. It is tempting because it durably instils a house style, and would be correct if many models or high-volume production traffic required permanent dialect alignment rather than a quick prompt fix.
- ✗
Provide a few-shot example of a formal French translation in the prompt.
Why it's wrong here
A few-shot example demonstrates the desired register but does not constrain the model's output distribution, so informal forms can still appear across long descriptions. It is tempting because examples reliably steer style and format, and would be correct if the model needed to match a bespoke output structure rather than enforce one dialect consistently.
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