Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A research team is using a large language model to analyze medical research papers and generate summaries. They need to minimize hallucinations while retaining key details. They have access to a curated database of paper abstracts. Which approach is best?
⚠ Common exam trap
Many candidates mistakenly think that fine-tuning or low temperature alone can solve hallucination, but the trap here is that without external retrieval (RAG), the model has no mechanism to verify facts against a trusted source, so it will still generate plausible-sounding but incorrect details.
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 RAG to retrieve relevant abstracts and incorporate them into the prompt.
Retrieval-Augmented Generation (RAG) directly addresses hallucination by grounding the model's output in a curated database of paper abstracts. By retrieving relevant abstracts and injecting them into the prompt, the model generates summaries based on verified facts rather than relying solely on its parametric knowledge, which is the most effective way to minimize hallucinations while retaining key details.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on the entire database of papers.
Why it's wrong here
Fine-tuning bakes the abstracts into model weights, which does not ground each generated summary in the source text and can still hallucinate; it also risks overfitting and staleness. Fine-tuning suits teaching a model a consistent output style or domain vocabulary, not retrieving facts from a curated corpus at inference time.
- ✗
Use chain-of-thought prompting to reason step-by-step.
Why it's wrong here
Chain-of-thought prompting elicits intermediate reasoning steps, which for summarisation adds generated content that is not anchored to the source and can introduce unsupported claims. It is the right technique for multi-step arithmetic, logic or planning problems where explicit intermediate reasoning improves accuracy, not for grounding summaries in a supplied corpus.
- ✗
Use few-shot prompting with examples of accurate summaries and set temperature=0.0.
Why it's wrong here
Few-shot prompting with temperature 0.0 reduces sampling randomness but still relies on parametric memory, so the model can invent details absent from the paper; it does not inject the curated abstracts. This approach suits tasks where the answer is already known to the model and consistent formatting matters more than factual grounding.
- ✓
Implement RAG to retrieve relevant abstracts and incorporate them into the prompt.
Why this is correct
RAG grounds generation in the curated abstracts by retrieving relevant passages and inserting them into the prompt, so the model summarises supplied evidence rather than relying on parametric memory. This directly minimises hallucination while retaining key details, satisfying the accuracy constraint.
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