Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A company uses a text generation model for customer support but notices it occasionally provides outdated information. Which technique should they implement to improve output accuracy?
⚠ Common exam trap
A common mix-up: candidates confuse fine-tuning (which adapts the model's weights to a static dataset) with RAG (which dynamically retrieves external knowledge), leading them to choose fine-tuning as a 'deeper' fix when the core issue is stale information, not model capability.
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 retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is the correct technique because it grounds the model's output in real-time, external knowledge sources (e.g., a vector database or document index) rather than relying solely on static training data. This directly addresses the problem of outdated information by allowing the model to retrieve and synthesize current facts at inference time, ensuring accuracy without requiring retraining.
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 max output tokens
Why it's wrong here
Token limit affects output length, not factual correctness.
- ✓
Implement retrieval-augmented generation (RAG)
Why this is correct
RAG retrieves current information, making outputs accurate and up-to-date.
- ✗
Fine-tune the model with more historical support data
Why it's wrong here
Historical data may not reflect recent changes; fine-tuning is not ideal for real-time updates.
- ✗
Increase model temperature to 1.0
Why it's wrong here
Higher temperature increases randomness, reducing accuracy.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 683 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 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.