Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A retail bank uses a generative AI assistant to answer customer questions about account policies. Compliance requires that every response cite the specific internal policy document section it used. Which approach best enforces this requirement?
⚠ Common exam trap
The trap here is believing that a strong instruction to be accurate or a low temperature setting produces verifiable citations, when only grounding the response in retrieved source documents can do that.
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
✓
Use retrieval-augmented generation to fetch policy passages and instruct the model to cite them.
A citation mandate is a grounding problem, not a sampling or behavior problem. Retrieval-augmented generation supplies authoritative policy passages at inference time and lets the prompt demand a citation for each. Temperature tuning, generic instructions, and fine-tuning either do not provide sources or cannot guarantee verifiable, current section references.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on the bank's policy manuals so it memorizes the sections.
Why it's wrong here
Fine-tuning can teach policy content, but memorized knowledge cannot reliably produce accurate section citations and becomes stale when policies change. The model may paraphrase without a verifiable source, which fails the compliance requirement. Retrieval keeps citations current and auditable, whereas fine-tuning obscures provenance and requires retraining on every policy update.
- ✓
Use retrieval-augmented generation to fetch policy passages and instruct the model to cite them.
Why this is correct
Retrieval-augmented generation grounds responses in retrieved internal documents and lets the prompt require a citation to the fetched section. This directly satisfies the compliance rule because the model answers from authoritative policy text rather than parametric memory. It also makes citations verifiable, since each claim maps to a retrievable passage the bank controls.
- ✗
Add a system instruction telling the model to never guess and to be accurate.
Why it's wrong here
A generic accuracy instruction does not give the model access to the policy documents or a mechanism to cite them. Instructions alone cannot guarantee grounded citations, and the model may still fabricate section numbers. Compliance needs traceable sources, which requires retrieval of the actual documents rather than a behavioral directive.
- ✗
Lower the model's temperature to zero so responses are deterministic.
Why it's wrong here
Temperature zero reduces randomness but does not make the model retrieve or cite internal policy sections. A deterministic response can still be uncited or fabricated. This setting controls sampling variability, not grounding, so it cannot satisfy a compliance mandate that each answer reference a specific source document.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 1,008 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.