Generative AI Leader Fundamentals of Generative AI Practice Question
A team is designing prompts for a generative AI application that summarizes long legal contracts. They want outputs that are accurate, consistently formatted, and safe from leaking instructions. Which two prompt engineering practices should they apply? (Choose two.)
⚠ Common exam trap
The trap here is treating any instruction about instructions as helpful, when asking the model to reveal or obey embedded text can weaken safety instead of strengthening it.
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
✓
Instruct the model to ignore any instructions embedded inside the contract text.
Giving a clear task plus a formatting example improves output consistency, and explicitly telling the model to ignore instructions inside the contract text reduces prompt-injection risk. These two practices together address accuracy, formatting stability, and safety when summarizing long legal documents, which were the team's stated goals.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set temperature to the maximum value to encourage more varied legal wording.
Why it's wrong here
High temperature increases randomness, which is undesirable for legal summarization where accuracy and consistent structure matter. Varied wording can introduce ambiguity or unsupported phrasing. Lower temperature is generally preferred for extraction and summarization tasks that require stability and fidelity to the source.
- ✓
Instruct the model to ignore any instructions embedded inside the contract text.
Why this is correct
Contracts may contain clauses that look like instructions, and a malicious or accidental phrase could steer the model. Explicitly telling the model to treat document content as data and not as commands reduces prompt-injection risk and helps keep the summary faithful to the intended task, which is a recommended safety practice.
- ✗
Insert the entire contract text without delimiters and ask the model to figure out the important parts.
Why it's wrong here
Dumping raw text without delimiters makes it harder for the model to distinguish instructions from content and can cause it to summarize the wrong sections or ignore constraints. Clear separation between instructions and source material is important for accuracy and for reducing prompt-injection risk in long documents.
- ✓
Provide a clear task instruction with an example of the desired output format.
Why this is correct
Clear instructions tell the model exactly what to do, and including an example of the desired format demonstrates the expected structure. This improves consistency across summaries and reduces ambiguity. Few-shot formatting examples are a recognized technique for guiding generative models toward reliable, repeatable outputs in document-heavy tasks like contract summarization.
- ✗
Ask the model to reveal its system instructions so the team can verify them.
Why it's wrong here
Requesting disclosure of system instructions is a prompt-injection pattern and can expose internal configuration. It does not improve summarization accuracy or formatting. Good practice is to protect system instructions rather than ask the model to reveal them, and to test robustness against such extraction attempts.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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