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Generative AI Leader Practice Question: A marketing team wants to generate social media…
A marketing team wants to generate social media posts in a consistent brand voice. They have a few examples of high-performing posts. Which prompt engineering technique should they use?
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
✓
Few-shot prompting with examples of past successful posts
Few-shot prompting provides the model with examples of the desired output style and tone, enabling consistent brand voice without fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Few-shot prompting with examples of past successful posts
Why this is correct
Few-shot prompting supplies the model with concrete examples of past high-performing posts, letting it infer tone, structure and vocabulary directly from them. This satisfies the brand-voice consistency constraint more reliably than zero-shot instructions, since patterns are demonstrated rather than described.
- ✗
Fine-tuning the model on all past social media posts
Why it's wrong here
Fine-tuning on all past posts needs a large labelled dataset and compute, and it bakes in old, off-brand examples rather than the few high-performing ones supplied. It is tempting because fine-tuning specialises a model, and would be correct with thousands of curated examples and a durable, reusable brand model.
- ✗
Zero-shot prompting with a detailed description of the brand voice
Why it's wrong here
Zero-shot prompting supplies only a description, so the model never sees the actual high-performing posts and cannot mirror their specific tone, phrasing or structure. It is tempting because it needs no examples, and would be correct when the brand voice is simple enough to specify fully in words without reference samples.
- ✗
Chain-of-thought prompting to explain reasoning
Why it's wrong here
Chain-of-thought elicits step-by-step reasoning, which suits arithmetic, logic or multi-step analysis, not stylistic imitation of a brand voice. It is tempting because it improves accuracy on complex tasks, and would be correct for problems needing decomposition, such as calculating campaign budgets or diagnosing faults.
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