Generative AI Leader Fundamentals of Generative AI Practice Question
A team is designing prompts for a generative AI model to summarize legal documents. They want to improve the quality and relevance of the summaries. Which two prompt engineering best practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is thinking that creative settings or minimal prompts improve specialized tasks like legal summarization.
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
✓
Include a few examples of well-written summaries in the prompt.
Clear instructions and few-shot examples are proven prompt engineering techniques that enhance output quality. They reduce ambiguity and guide the model toward the desired format and content. Other options like high temperature or step-by-step reasoning are not appropriate for precise summarization tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep the prompt as short as possible to avoid confusing the model.
Why it's wrong here
While conciseness can be good, overly short prompts may lack necessary context or instructions, leading to vague summaries. For complex tasks like legal summarization, detailed guidance is often needed. The goal is clarity, not minimal length, to ensure the model understands the requirements.
- ✓
Include a few examples of well-written summaries in the prompt.
Why this is correct
Few-shot prompting with examples demonstrates the desired format and style, helping the model generalize. For legal summaries, examples can show how to extract key clauses and maintain neutrality. This technique improves consistency and accuracy without fine-tuning, making it a best practice for specialized tasks.
- ✗
Ask the model to think step by step before providing the summary.
Why it's wrong here
Step-by-step reasoning can help with complex reasoning tasks, but for summarization, it may not be necessary and could introduce extraneous content. The model might include its reasoning in the output, which is undesirable. This technique is more suited for problem-solving prompts, not straightforward summarization.
- ✓
Provide clear and specific instructions about the desired summary length and focus.
Why this is correct
Clear instructions help the model understand the task, reducing ambiguity. Specifying length and focus guides the model to produce relevant summaries, improving quality. This is a fundamental prompt engineering practice that aligns model output with user intent, especially for complex documents where default summaries may miss key points.
- ✗
Use the highest possible temperature setting to encourage creativity.
Why it's wrong here
High temperature increases randomness and creativity, which is undesirable for legal summaries that require precision and consistency. It can lead to hallucinations or off-topic content. For summarization, lower temperatures are typically better to ensure factual and focused outputs.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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