Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A research team uses a generative AI model to answer questions about internal technical documents. The model sometimes provides outdated information because it relies on its pretrained knowledge instead of the latest documents. The team wants the answers to be based on the current document set. Which technique should they implement?
⚠ Common exam trap
The trap here is assuming that a prompt instruction or temperature change can make the model use current documents, when it actually needs the documents to be retrieved and provided.
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
✓
Retrieval-augmented generation that fetches relevant passages from the current document set and includes them in the prompt.
Retrieval-augmented generation is the right technique because it dynamically fetches relevant passages from the current document set and includes them in the prompt. This grounds the model in up-to-date information without retraining, directly solving the outdated-answer problem. Other options either change randomness, require costly retraining, or rely on an instruction the model cannot fulfill without access to the documents.
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 internal documents to update its knowledge.
Why it's wrong here
Fine-tuning can embed document knowledge, but it is costly and must be repeated whenever documents change. It also risks catastrophic forgetting and may not guarantee that the latest version is used. The scenario implies a need for ongoing currency. Retrieval-augmented generation is more flexible because it uses the live document set at query time without retraining.
- ✗
Increase the model's temperature so it relies less on memorized facts.
Why it's wrong here
Temperature controls randomness, not the source of factual content. A higher temperature can make the model more creative and potentially less accurate. It does not cause the model to prefer current documents over pretrained knowledge. The team needs a mechanism that injects current information, not a sampling change that could increase errors.
- ✓
Retrieval-augmented generation that fetches relevant passages from the current document set and includes them in the prompt.
Why this is correct
Retrieval-augmented generation supplies the model with up-to-date passages at inference time, so answers are grounded in the current documents rather than pretrained memory. This directly solves the outdated-information problem without retraining. It also allows the document set to be updated independently of the model. The model uses the retrieved context to generate accurate, source-based responses.
- ✗
Add a prompt instruction telling the model to always use the most recent information it knows.
Why it's wrong here
The model cannot know which information is most recent unless that information is provided. A prompt instruction alone does not give it access to the current document set. It may still rely on pretrained knowledge or hallucinate. The team needs to supply the current documents through retrieval or context injection, not just ask the model to be up to date.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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