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Generative AI Leader Practice Question: Using a fine-tuned LLM for generating technical…
A company is using a fine-tuned LLM for generating technical support responses. After deployment, they notice that the model sometimes produces incorrect but plausible-sounding answers (hallucinations). They have a large repository of verified technical manuals. Which technique would BEST reduce hallucinations while minimizing the need for additional training?
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
✓
Implement RAG by indexing the verified manuals and retrieving relevant sections during inference
RAG with the verified manuals as the knowledge base allows the model to ground its responses in authoritative sources without retraining. Fine-tuning might still hallucinate if the data is insufficient. Prompt engineering alone cannot eliminate hallucinations. Using a larger context window may help but is not as reliable as RAG.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the temperature parameter to make the model more conservative
Why it's wrong here
Raising temperature increases sampling randomness, which makes hallucinations more frequent, not less. Temperature controls output diversity, so it is the wrong lever entirely. The manuals go unused. Lowering temperature would only make wording more deterministic, not ground answers in verified content.
- ✗
Use a larger base model with a longer context window
Why it's wrong here
A larger context window only lets more text be supplied; it does not itself retrieve or ground answers in the manuals, so hallucinations persist. Larger models are chosen for reasoning capacity or long-document tasks, not for factual grounding against a verified repository.
- ✗
Fine-tune the model again with a larger dataset of verified responses
Why it's wrong here
Retraining on verified responses requires substantial compute, labelled data and time, contradicting the requirement to minimise additional training. Fine-tuning adjusts model weights for style or domain tone; it cannot cite the manuals at inference time, and stale weights still hallucinate.
- ✓
Implement RAG by indexing the verified manuals and retrieving relevant sections during inference
Why this is correct
RAG grounds generation in retrieved passages from the verified manuals, so the model conditions its output on authoritative text rather than parametric memory alone. This reduces plausible-sounding fabrications while requiring only indexing, not additional training.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.