Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A legal firm uses a generative AI to draft contracts. They want the output to follow a specific clause structure. Which technique should they use in the prompt?
⚠ Common exam trap
Candidates may confuse structural/format control (system instructions) with content control (grounding, temperature) or output termination (stop sequences). In Google Gen AI, system instructions are the primary method to enforce output structure, not grounding which pulls external data.
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 system instruction that defines the required format.
A system instruction (or system message) sets the overall behavior and output format for the generative AI model, effectively constraining it to follow a specific clause structure. This is the most direct and reliable technique for enforcing a predefined format in the prompt, as it operates at the model's instruction-following layer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Include a system instruction that defines the required format.
Why this is correct
A system instruction sets persistent behavioural constraints applied before user turns, so the model reliably follows the firm's clause structure across every draft. Embedding the format in the system role enforces consistency that one-off prompt wording cannot guarantee.
- ✗
Increase temperature to encourage variance.
Why it's wrong here
Raising temperature increases sampling randomness, producing varied and less predictable wording rather than enforcing a fixed clause order. It is tempting because temperature tuning is a common prompt control, but it would be correct when brainstorming or generating diverse drafts, not when the output must conform to a prescribed structure.
- ✗
Use grounding to pull from a database of contracts.
Why it's wrong here
Grounding supplies retrieved source content; it does not impose a clause structure on generated text. Specifying the desired clause order and headings in the prompt itself is what shapes output format. Grounding suits factual accuracy against a contract repository, not structural conformance.
- ✗
Set stop sequences to end generation at certain points.
Why it's wrong here
Stop sequences halt generation when specified tokens appear, controlling where output ends rather than how clauses are ordered or labelled. It is tempting because they shape output boundaries, but they would be correct when truncating a response before an unwanted continuation, not for imposing a required clause structure.
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