AI-102 Implement generative AI solutions Practice Question
You are using Azure OpenAI Service to summarize customer emails. The summaries must be concise and contain only key information. Which prompt engineering technique should you apply?
⚠ Common exam trap
Watch out — candidates often assume a simple instruction (zero-shot) is sufficient for summarization, underestimating how much the model relies on explicit examples to enforce output structure and conciseness, especially when the task requires domain-specific key information extraction.
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
✓
Use few-shot prompting with examples of desired summaries
Few-shot prompting is the correct technique because it provides the model with explicit examples of desired input-output pairs (e.g., a verbose email and its concise summary). This guides the model to learn the exact format, tone, and level of detail required for the summaries, which is critical for consistency in a production summarization pipeline. Without examples, the model may default to its training distribution and produce overly verbose or irrelevant output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use few-shot prompting with examples of desired summaries
Why this is correct
Few-shot prompting supplies the model with paired examples of emails and their concise summaries, directly demonstrating the desired length and content selection. This satisfies the stem's constraint that summaries contain only key information, since the exemplars establish the pattern the model imitates rather than relying on vague instructions alone.
- ✗
Use chain-of-thought prompting
Why it's wrong here
Chain-of-thought elicits intermediate reasoning steps, lengthening output rather than compressing it into key points. It tempts because it improves accuracy on multi-step tasks, and is the right choice for arithmetic, logic or diagnostic problems where the reasoning path matters.
- ✗
Use zero-shot prompting with a one-sentence instruction
Why it's wrong here
A bare one-sentence instruction gives the model no format, length or content constraints, so summaries vary and may include extraneous detail. It tempts because it is the minimal prompt, and it suffices for simple, well-defined tasks where the desired output is unambiguous.
- ✗
Use negative prompting to avoid verbose output
Why it's wrong here
Negative prompting lists behaviours to avoid but does not constrain output length or enforce selection of key content, so verbosity can persist. It tempts because suppressing unwanted phrasing feels targeted, and it works when excluding specific words, formats or topics from a response.
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Senior Network & Security Engineer · founder of Courseiva
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