Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A developer is building a customer support chatbot using a large language model. The chatbot frequently generates plausible-sounding but incorrect answers to product questions. Which technique should be applied to improve factual accuracy?
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
✓
Enable Grounding with the company's product knowledge base.
Grounding (e.g., using Vertex AI Grounding with Search) retrieves relevant information from a trusted source in real time, reducing hallucination. Option A is wrong because few-shot prompting can help but only if examples are accurate and relevant; it does not dynamically look up facts. Option B is wrong because higher temperature increases randomness, worsening hallucinations. Option C is wrong because increasing context length may include more irrelevant information and does not guarantee accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Provide a few-shot example of correct answers in the prompt.
Why it's wrong here
Few-shot can improve format but doesn't guarantee factual correctness for new queries.
- ✗
Use a higher temperature setting to encourage more creative responses.
Why it's wrong here
Higher temperature increases randomness and likelihood of hallucinations.
- ✗
Increase the model's context length to include more of the conversation history.
Why it's wrong here
More context can confuse the model and doesn't enforce factual retrieval.
- ✓
Enable Grounding with the company's product knowledge base.
Why this is correct
Grounding retrieves live, verified data and injects it into the prompt, directly improving factual accuracy.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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