Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team wants to improve the factual accuracy of their chatbot responses regarding internal company policies. What is the most effective approach?
⚠ Common exam trap
A common misconception in the Google Gen AI Leader exam is that fine-tuning (Option C) is the best approach to improve factual accuracy for dynamic knowledge. In reality, RAG with Vertex AI Search is superior because it retrieves up-to-date policies from a curated index without retraining the model, and provides verifiable source citations.
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 RAG with Vertex AI Search indexing the policies
RAG with Vertex AI Search is the most effective approach because it retrieves relevant, up-to-date policy documents from a curated index and injects them into the prompt context at inference time, grounding the chatbot's responses in authoritative sources without modifying the underlying model. This ensures factual accuracy for dynamic or evolving policies, as the model can reference the exact text rather than relying on static training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use few-shot prompting with example Q&A pairs
Why it's wrong here
Few-shot prompting supplies example Q&A pairs that steer format and tone, but the model still answers from parametric memory, so internal policy facts remain unverified. It is tempting because few-shot prompting cheaply improves task alignment, yet grounding answers in a retrieved policy knowledge base is required for factual accuracy.
- ✗
Increase the model's maximum tokens
Why it's wrong here
Raising maximum tokens only extends output length; it adds no policy knowledge and can increase fabricated detail. It is tempting because longer outputs appear more complete, but the requirement is factual grounding in internal documents, which retrieval-augmented generation provides by supplying authoritative source text at inference time.
- ✗
Fine-tune the model on policy documents
Why it's wrong here
Fine-tuning on policy documents bakes facts into weights, which drift as policies change and still permits hallucination; retrieval keeps answers tied to current source text. It is tempting because fine-tuning customises tone and domain vocabulary, but for frequently updated internal policies, retrieval-augmented generation is the correct approach.
- ✓
Use RAG with Vertex AI Search indexing the policies
Why this is correct
RAG retrieves relevant passages from the indexed policy corpus at query time and supplies them as grounding context, so answers cite actual internal documents rather than relying on parametric memory. Vertex AI Search handles indexing and retrieval, satisfying the requirement for factual accuracy on company-specific policies.
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Written by Johnson Ajibi, MSc IT Security
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