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Generative AI Leader Practice Question: Deciding between building a custom fine-tuned…
A company is deciding between building a custom fine-tuned model vs. using a pre-built API for a document summarization task. The documents contain domain-specific jargon. Which factor STRONGLY favors using a pre-built API with prompt engineering?
⚠ Common exam trap
Google often tests the misconception that prompt engineering can fully replace fine-tuning for domain adaptation, when in reality prompt engineering is limited by context window size and cannot permanently encode specialized knowledge or writing styles.
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
✓
The requirement for low initial development cost and fast time-to-market
Using a pre-built API with prompt engineering eliminates the need for expensive model training infrastructure and specialized ML expertise, enabling rapid deployment at low initial cost. For a document summarization task, prompt engineering can leverage the API's existing capabilities without custom fine-tuning, making it ideal when speed and budget are primary constraints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The need to handle highly specialized industry terminology
Why it's wrong here
Specialised terminology is precisely what fine-tuning addresses by training on domain corpora; prompt engineering cannot reliably teach unfamiliar jargon. This factor favours fine-tuning, and would favour an API only if the terminology were already represented in the base model's training data.
- ✗
The need for the model to learn a unique writing style from past summaries
Why it's wrong here
Learning a unique writing style from past summaries requires training on those examples, which is fine-tuning; prompts can imitate style only superficially. This factor favours fine-tuning, and would favour an API only where style consistency across many outputs is not required.
- ✗
The need to keep all data on-premises for security compliance
Why it's wrong here
On-premises data residency cannot be met by a pre-built API hosted by a vendor; the documents would leave the environment, so this favours a self-hosted or fine-tuned model instead. It would favour an API only where compliance permits vendor-hosted processing under an approved agreement.
- ✓
The requirement for low initial development cost and fast time-to-market
Why this is correct
Low initial development cost and fast time-to-market strongly favour the pre-built API, since prompt engineering requires no labelled dataset, training compute, or model hosting. Fine-tuning demands curated domain examples and GPU training cycles, delaying deployment. This directly satisfies the stem's constraint: summarising jargon-heavy documents without the overhead of custom model development.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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