Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A company is using a generative AI model to generate product descriptions. They notice the outputs often include factual inaccuracies about product specifications. Which technique would best address this issue without modifying the model's architecture?
⚠ Common exam trap
Google Cloud often tests the misconception that adjusting generation parameters (like temperature or token limits) or providing examples can fix factual accuracy, when in fact only retrieval-augmented methods or fine-tuning on verified data can correct hallucinations without changing the model architecture.
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 a Retrieval-Augmented Generation (RAG) pipeline that retrieves product specs from a database
Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's output in factual, up-to-date product specifications retrieved from an external database. This directly addresses factual inaccuracies without modifying the model's architecture, as the model generates text based on retrieved context rather than relying solely on its parametric knowledge.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves product specs from a database
Why this is correct
RAG grounds generation in retrieved relevant documents, improving factual accuracy.
- ✗
Decrease the temperature parameter to 0.1
Why it's wrong here
Reducing temperature makes outputs less random but does not provide factual grounding.
- ✗
Increase the max output tokens to 1024
Why it's wrong here
Increasing token limit only allows longer responses, not more accurate facts.
- ✗
Use few-shot prompting with 5 examples of correct descriptions
Why it's wrong here
Few-shot prompting improves adherence to style but does not guarantee factual correctness.
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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.