Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO techniques are most effective for improving factual accuracy in a generative AI model's responses? (Choose two.)
⚠ Common exam trap
The trap is confusing fine-tuning with factual grounding; candidates assume training on more data improves facts, but fine-tuning on general text teaches patterns, not verified truths — only retrieval and grounding inject authoritative facts at inference time.
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
✓
Retrieval-Augmented Generation (RAG) with curated datasets.
Option A, Retrieval-Augmented Generation (RAG) with curated datasets, is correct because RAG retrieves relevant, up-to-date passages from an external curated corpus at inference time and injects them into the context, so the model's answer is anchored to verifiable source content rather than relying solely on parametric memory, which directly reduces hallucination and improves factual accuracy. Option C, grounding with a trusted knowledge base, is correct because grounding constrains generation to authoritative, domain-validated sources (e.g., enterprise knowledge bases or vetted document stores), letting the model cite or align with established facts and thereby improving correctness and traceability. Option B is wrong because raising temperature to 1.5 increases sampling randomness and diversity, which makes outputs less deterministic and more prone to fabrication, not more factual. Option D is wrong because longer system prompts with many instructions can dilute attention, introduce conflicting directives, and do not supply new factual evidence, so they are unreliable for accuracy. Option E is wrong because fine-tuning on a large general-text corpus mainly adapts style and broad language patterns and can even reinforce outdated or incorrect parametric knowledge, whereas accuracy gains require targeted, high-quality, task-specific data or retrieval.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieval-Augmented Generation (RAG) with curated datasets.
Why this is correct
RAG retrieves relevant passages from a curated corpus at inference time and injects them into the prompt, so responses cite grounded evidence rather than relying solely on parametric memory. The curated datasets constraint directly reduces hallucination and improves factual accuracy.
- ✗
Increasing the model's temperature to 1.5.
Why it's wrong here
Raising temperature to 1.5 increases sampling randomness, producing more varied but less deterministic output, which amplifies hallucination rather than grounding responses in fact. Higher temperature suits creative drafting or brainstorming tasks where diversity of phrasing matters, not factual question answering that demands accurate, reproducible answers.
- ✓
Grounding with a trusted knowledge base.
Why this is correct
Grounding constrains generation to content retrieved from a trusted knowledge base, so the model's output is anchored to verifiable source material rather than latent weights. This satisfies the accuracy requirement by supplying authoritative context at inference time.
- ✗
Using longer system prompts with multiple instructions.
Why it's wrong here
Longer system prompts with many instructions do not supply the model with verifiable external knowledge, so factual gaps remain and instruction dilution can reduce compliance. Elaborate system prompts suit controlling tone, format and persona, whereas accuracy requires retrieval-augmented generation or grounding the model in authoritative source documents.
- ✗
Fine-tuning on a large corpus of general text.
Why it's wrong here
Fine-tuning on general text teaches style and format, not factual grounding, and can reinforce existing errors. It is tempting because fine-tuning adapts model behaviour, but factual accuracy improves through retrieval-augmented generation or fine-tuning on curated, verified domain data, not broad general corpora.
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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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