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AI Associate AI Fundamentals Practice Question

A company uses AI to generate personalized email content for marketing campaigns. They notice the AI occasionally produces factually incorrect statements. Which THREE actions should they take to mitigate this?

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 retrieval-augmented generation (RAG) to ground outputs in verified data

Grounding the model with retrieval, adding a human review step, and adjusting temperature can reduce hallucinations. Training on more data alone may not fix the issue.

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 retrieval-augmented generation (RAG) to ground outputs in verified data

    Why this is correct

    RAG provides factual context, reducing hallucinations.

  • Reduce the model's temperature parameter to lower randomness

    Why this is correct

    Lower temperature makes outputs more deterministic and less prone to fabrications.

  • Include a human review step before emails are sent

    Why this is correct

    Human oversight catches errors before they reach customers.

  • Train the model on more unlabeled emails from the internet

    Why it's wrong here

    More unlabeled data may introduce more noise, not reduce hallucinations.

  • Remove all safety guardrails to allow more creative content

    Why it's wrong here

    Removing guardrails increases risk of hallucinations and harmful content.

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