Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A team is deploying a text generation model for legal document review. They observe that the model occasionally generates factually incorrect legal citations. Which approach best reduces this issue?
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) with a verified legal database.
Retrieval-augmented generation (RAG) with a verified legal database grounds the model in factual, up-to-date sources, directly addressing incorrect citations. Option B (lowering temperature) reduces randomness but does not prevent hallucination. Option C (using a larger model) may not guarantee correctness without proper grounding. Option D (increasing max tokens) has no effect on factual 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.
- ✓
Implement retrieval-augmented generation (RAG) with a verified legal database.
Why this is correct
RAG retrieves relevant passages from a verified legal database and supplies them as context, grounding the model's output in authoritative source material. This constrains generation to cited, verifiable content, directly reducing fabricated legal citations that arise from the model's parametric memory alone.
- ✗
Lower the temperature to 0.0.
Why it's wrong here
Temperature 0.0 makes token selection deterministic, picking the highest-probability token each step, but a confidently wrong citation remains the top candidate, so fabrication persists. It suits tasks needing reproducible, low-variance output, such as classification or extraction, not grounding claims in verified legal sources.
- ✗
Use a larger base model.
Why it's wrong here
A larger base model may reduce error rates but still generates citations from parametric memory, which cannot guarantee verifiable references. Larger models suit broad reasoning and language coverage; grounding citations in an authoritative legal database through retrieval is what prevents fabrication.
- ✗
Increase the max output tokens.
Why it's wrong here
Max output tokens caps generation length only; it neither retrieves nor verifies citations, so fabricated references still appear within the allowed span. Raising it suits long-form outputs like full contract summaries, whereas reducing hallucinated citations requires retrieval-augmented generation grounded in a verified legal corpus.
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