Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which THREE are best practices for designing prompts for a generative AI model?
⚠ Common exam trap
A common misconception tested in Google's Gen AI evaluations is that negative prompts can reliably control outputs, but they often fail due to tokenization and probability smoothing, leading to the 'forbidden token' problem where undesired content still appears.
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 few-shot examples for complex tasks
Providing few-shot examples (e.g., 2-5 input-output pairs) helps the model infer the desired pattern, reducing ambiguity for complex tasks like classification or structured extraction. This technique leverages in-context learning, where the model uses the examples as a template without fine-tuning.
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 few-shot examples for complex tasks
Why this is correct
Correct: Examples guide the model toward desired outputs.
- ✓
Include specific and clear instructions
Why this is correct
Correct: Clarity reduces ambiguity.
- ✓
Break the task into smaller steps
Why this is correct
Correct: Step-by-step prompts improve reasoning and accuracy.
- ✗
Use negative prompts to avoid undesired outputs
Why it's wrong here
Negative prompting can backfire; focusing on positives is better.
- ✗
Always set temperature to 1.0 for creativity
Why it's wrong here
Temperature should be tuned per task, not fixed.
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