Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A chatbot built with Vertex AI PaLM API often provides outdated information about company policies because the training data is months old. Which approach should the team use?
⚠ Common exam trap
In the Google Gen AI Leader exam, a common trap is confusing grounding (dynamic knowledge injection at inference time) with fine-tuning (static model update). Candidates often assume fine-tuning is the best solution for real-time accuracy, but grounding is the correct approach when policies change frequently.
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 grounding by connecting to a knowledge base of current policies.
Grounding connects the PaLM API to a live, authoritative knowledge base (e.g., Cloud Storage, BigQuery, or Vertex AI Search) containing the latest company policies. This allows the model to retrieve and cite current information at inference time without retraining, directly solving the staleness issue. Grounding is the recommended approach in Vertex AI for ensuring factual, up-to-date responses from a foundation model.
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 grounding by connecting to a knowledge base of current policies.
Why this is correct
Grounding retrieves current policy documents at inference time and passes them as context, so responses reflect the live knowledge base rather than the model's stale training data. This satisfies the requirement for up-to-date company policy information without retraining the PaLM model.
- ✗
Use prompt engineering to instruct the model to say 'I don't know' if unsure.
Why it's wrong here
Prompt engineering cannot supply policy facts absent from the model's training data; instructing it to say 'I don't know' reduces confident errors but still yields no current policy answer. It tempts because prompting is the cheapest first lever for steering tone and refusals, and is correct when the issue is behaviour rather than missing knowledge.
- ✗
Increase the context window to include more history.
Why it's wrong here
Extending the context window only lets the model attend to more tokens per prompt; it cannot inject policy documents the model never saw during pre-training, so stale answers persist. It is tempting because a larger context window is genuinely useful for grounding answers in retrieved documents supplied at inference time.
- ✗
Fine-tune the model on the latest policy documents.
Why it's wrong here
Fine-tuning adjusts weights toward the supplied examples but does not reliably store retrievable, updatable policy facts, and retraining per policy change is impractical. It is tempting because fine-tuning genuinely suits teaching tone, format or task behaviour, where the goal is style rather than current factual recall.
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