Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A large e-commerce company deploys a generative AI chatbot on Vertex AI for customer service. The chatbot is powered by a fine-tuned model on the company's historical support tickets. Despite high accuracy on training topics, the chatbot frequently gives irrelevant or off-topic answers when customers ask about new products or promotions. The company maintains a comprehensive product catalog and a knowledge base of current promotions. The chatbot's prompts include a system instruction to 'Answer based on your knowledge' and no other retrieval mechanism. The response time requirement is under 3 seconds. Which course of action should the team take?
⚠ Common exam trap
Google often tests the misconception that fine-tuning alone can solve knowledge gaps for dynamic or time-sensitive data, when in reality RAG is the appropriate technique for incorporating external, frequently updated information without retraining.
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 RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.
Implementing a RAG (Retrieval-Augmented Generation) pipeline directly addresses the chatbot's inability to answer questions about new products or promotions. By retrieving relevant, up-to-date information from the company's product catalog and knowledge base and injecting it into the prompt, the model gains access to current data beyond its training cutoff. This approach keeps response times under 3 seconds (as retrieval is fast) and avoids the need for costly retraining, while the system instruction 'Answer based on your knowledge' is replaced with grounded context.
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 RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.
Why this is correct
Retrieval-augmented generation fetches current product and promotion content from the knowledge base and injects it into the prompt, grounding answers in up-to-date facts the fine-tuned model never saw. This addresses the off-topic responses while keeping latency within the three-second requirement.
- ✗
Increase the temperature to encourage the model to generate more diverse answers.
Why it's wrong here
Temperature controls sampling randomness, so raising it produces more varied wording rather than grounding answers in the catalogue and promotions. It is tempting because diverse output can appear richer, but the failure is missing factual context, which higher temperature worsens rather than fixes.
- ✗
Add additional safety filters to block irrelevant responses.
Why it's wrong here
Safety filters suppress harmful or disallowed output; they cannot supply current product or promotion facts the model never learned. Filtering is tempting as a guardrail against off-topic replies, but the gap is missing retrieval of the catalogue and promotions, not unsafe content.
- ✗
Fine-tune the model again on a larger dataset that includes recent support tickets.
Why it's wrong here
Fine-tuning bakes static patterns into weights, so newly added products and promotions remain unknown until the next training run, and retraining cannot keep pace with changing catalogues. It is tempting because the model was fine-tuned originally, but retrieval-augmented generation is what injects current facts at inference time.
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