Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A company notices that their AI chatbot occasionally generates incorrect information. Which technique can best reduce hallucinations without retraining?
⚠ Common exam trap
Google often tests the misconception that adjusting sampling parameters (like top_p or temperature) can fix hallucinations, when in reality these parameters control randomness, not factual grounding, and the correct solution is to constrain the model's output to a trusted context.
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 system instructions to constrain the model to only answer from provided context
Constraining the model to answer only from provided context directly addresses the root cause of hallucinations—the model generating information not grounded in verified sources. This technique, often implemented via system instructions or retrieval-augmented generation (RAG) pipelines, forces the model to rely on a trusted knowledge base rather than its parametric memory, effectively eliminating unsupported fabrications without requiring retraining.
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 a longer system prompt without examples
Why it's wrong here
A longer prompt without examples adds instructions but no grounding evidence, so the model still fabricates when its parametric knowledge is absent or stale. Few-shot examples or retrieval-augmented generation supply that evidence; prompt length alone does not anchor factual claims.
- ✓
Use system instructions to constrain the model to only answer from provided context
Why this is correct
System instructions steer generation by restricting the model to answer solely from supplied context, so unsupported claims are refused rather than invented. This constrains decoding behaviour at inference time, reducing hallucinations without any retraining, fine-tuning or modification of model weights.
- ✗
Set top_p to 0.1
Why it's wrong here
Nucleus sampling at top_p 0.1 restricts token choice to a narrow high-probability set, which reduces diversity but does not supply missing facts, so the model can still assert wrong information confidently. Retrieval-augmented generation grounds answers in source documents, which is what prevents fabrication.
- ✗
Increase temperature to 0.9
Why it's wrong here
Raising temperature to 0.9 increases sampling randomness, producing more varied and less factual output, so hallucinations typically worsen. Temperature controls creativity for brainstorming or marketing copy; reducing it toward zero is what constrains the model to likely tokens.
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