Generative AI Leader Fundamentals of Generative AI Practice Question
You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing output length or adding external data automatically improves quality, when in fact the most direct and cost-effective fix is to refine the input prompt to guide the model's focus.
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
✓
Revise the prompt to explicitly ask for medication dosages and allergies, and format the input text by adding headings (e.g., '### Medications') to emphasize important sections.
Prompt engineering is the most effective and cost-efficient way to improve LLM output without retraining or changing the model. By explicitly instructing the model to include medication dosages and allergies, and by structuring the input with clear headings, you guide the model's attention to critical sections, directly addressing the missing details. This approach leverages the LLM's existing capabilities and requires no changes to the hosted API or additional infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum output token limit to force the model to include more details.
Why it's wrong here
Raising the output token ceiling only permits longer text; it does not direct the model towards dosages or allergies, so omissions persist. It is tempting when summaries are truncated mid-sentence, where the limit genuinely is the binding constraint, but here content is missing rather than cut off.
- ✗
Replace the LLM with a simpler extractive summarization model that selects sentences from the original document.
Why it's wrong here
Extractive selection copies whole sentences, so dosages and allergies appear only if already phrased as standalone sentences, and it cannot condense or reconcile scattered mentions. It is tempting for cheap, faithful summarisation of well-structured documents, but it sacrifices the abstraction that clinical summarisation requires.
- ✗
Implement a retrieval-augmented generation (RAG) system that pulls supplementary data from external drug databases.
Why it's wrong here
Retrieval from external drug databases injects pharmacology reference material, not the patient's own record, so dosages and allergies recorded in the EHR remain unsummarised. It is tempting because RAG grounds answers in authoritative sources, which is correct when the gap is missing external knowledge rather than overlooked input text.
- ✓
Revise the prompt to explicitly ask for medication dosages and allergies, and format the input text by adding headings (e.g., '### Medications') to emphasize important sections.
Why this is correct
Prompt engineering and structured input formatting directly address the omission of dosages and allergies without touching the managed API model, satisfying the no-retraining constraint. Explicit instructions plus section headings steer the LLM's attention to clinically critical fields, a low-cost, rapid fix suited to the limited time and budget.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.