Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is using a language model for customer feedback analysis. They want to improve the accuracy of sentiment extraction. Which TWO techniques should they apply? (Choose two.)
⚠ Common exam trap
A common misconception is that increasing temperature or enforcing output format directly improves accuracy, but these techniques affect creativity or structure, not the correctness of the underlying classification.
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 of correctly labeled sentiment in the prompt.
Two complementary techniques improve sentiment extraction accuracy. First, providing few-shot examples of correctly labeled sentiment in the prompt guides the model through in-context learning, clarifying the desired output format and classification boundaries without retraining. Second, fine-tuning the model on a labeled dataset of customer feedback adapts the model's weights to the specific domain and label distribution, yielding more accurate and consistent sentiment classification than prompting alone. Increasing temperature, using a generic built-in sentiment API, or enforcing JSON output format do not directly improve classification accuracy.
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 0.8 to allow more creative interpretations.
Why it's wrong here
Higher temperature increases randomness, reducing accuracy.
- ✓
Provide few-shot examples of correctly labeled sentiment in the prompt.
Why this is correct
Few-shot examples guide the model's output format and accuracy.
- ✗
Use the model's built-in sentiment analysis API instead of prompting.
Why it's wrong here
This is a different tool, not a technique to improve the current model.
- ✗
Add a system instruction that asks the model to strictly follow JSON output format.
Why it's wrong here
Formatting does not improve sentiment extraction accuracy.
- ✓
Fine-tune the model on a labeled dataset of customer feedback.
Why this is correct
Fine-tuning adapts the model to the specific task.
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