Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A healthcare company is using a fine-tuned version of PaLM 2 on Vertex AI to generate clinical notes from doctor-patient conversations. The model was fine-tuned on a dataset of 10,000 de-identified transcripts and corresponding notes. During testing, the generated notes are grammatically correct and well-structured, but they often contain subtle inaccuracies: for example, they might mention a medication that was not discussed, or omit a key symptom. The team has already tried increasing the training epochs and adjusting learning rates, with minimal improvement. They need a solution that can be implemented quickly to improve factual accuracy without retraining the entire model. The team has access to a large archive of verified clinical notes and a small set of recent conversation-to-note pairs that have been manually reviewed and corrected. The inference pipeline currently uses a single call to the model with the conversation transcript as input. What should the team do?
⚠ Common exam trap
Test-takers frequently assume factual inaccuracy is solely a randomness issue (temperature) or a prompt instruction problem, overlooking that the model's parametric knowledge is insufficient and needs external grounding via retrieval augmentation.
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) by retrieving similar verified notes from the archive and providing them as context in the prompt.
Retrieval-augmented generation (RAG) directly addresses the core issue of factual inaccuracy without retraining. By retrieving verified clinical notes similar to the current conversation from the archive and injecting them as context in the prompt, the model gains access to ground-truth examples that anchor its output to factual details. This approach leverages the team's existing archive and small set of corrected pairs to provide relevant, accurate context, improving precision without modifying the model's weights.
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) by retrieving similar verified notes from the archive and providing them as context in the prompt.
Why this is correct
RAG grounds the generation in factual examples, directly reducing inaccuracies without retraining.
- ✗
Decrease the temperature to 0.1 to reduce randomness and force the model to stick to the input.
Why it's wrong here
Low temperature reduces randomness but does not add factual grounding; the model may still omit or invent details.
- ✗
Use prompt engineering to instruct the model to only include information explicitly mentioned in the conversation.
Why it's wrong here
Prompt engineering helps but may not overcome the model's training biases; it can still hallucinate.
- ✗
Add a human-in-the-loop step to review and correct every generated note before use.
Why it's wrong here
Human review is slow and doesn't reduce the model's tendency to produce inaccuracies; it's a workaround not a fix.
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