AIF-C01 Fundamentals of Generative AI Practice Question
A media company uses a foundation model on Amazon Bedrock to generate article summaries. The model occasionally omits important details. Which prompt engineering technique is most likely to improve completeness?
⚠ Common exam trap
A common misconception is that adjusting model parameters like temperature or token limits can fix content quality issues like missing details. In practice, prompt structure and explicit instructions, such as listing required key points, are the primary tools for controlling output completeness and accuracy.
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
✓
Include a list of required key points in the prompt
Explicitly listing required key points in the prompt guides the foundation model to cover all specified elements, directly addressing the omission issue. This technique, often called 'constrained generation' or 'structured prompting,' forces the model to attend to each required detail, improving completeness without altering model parameters.
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 a lower temperature setting
Why it's wrong here
Lower temperature reduces randomness in token selection, making output more deterministic but not more comprehensive; omitted details stem from prompt scope, not sampling variance. It is tempting because determinism sounds like accuracy, and would be correct where the requirement is consistent, reproducible responses.
- ✗
Increase the max tokens limit
Why it's wrong here
Raising max tokens only extends the allowed output length; it does not make the model include details it was not prompted to cover. It is tempting because truncation looks like omission, and would be correct where summaries are being cut off mid-sentence by the output token ceiling.
- ✓
Include a list of required key points in the prompt
Why this is correct
Listing required key points in the prompt explicitly constrains the model's output coverage, directing it to address each specified element rather than relying on its own judgement of relevance. This directly counteracts the omission of important details by making completeness an explicit instruction.
- ✗
Add 'Be concise' to the prompt
Why it's wrong here
Instructing the model to be concise directly suppresses detail, worsening the omission of important content. It is tempting because brevity suits summary length limits, and would be correct where the requirement is to shorten output or cap response length rather than improve completeness.
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