AIF-C01 Applications of Foundation Models Practice Question
A media company uses Amazon Bedrock to generate article summaries. They notice that for long articles, the model sometimes ignores instructions placed at the beginning of the prompt. The company wants to improve the model's adherence to instructions without changing the model or increasing cost significantly. Which prompt engineering technique should they apply?
⚠ Common exam trap
The trap here is thinking that increasing maxTokenCount or temperature will fix instruction-following, when the real issue is prompt structure and attention placement.
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
✓
Move the instructions to the end of the prompt and repeat them after the article text.
The model's tendency to overlook early instructions in long contexts is a known limitation. By moving instructions to the end of the prompt and repeating them after the article, the company places them where the model's attention is strongest. This prompt engineering technique improves adherence without retraining or increasing cost, making it the most effective solution.
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 higher temperature value to make the model more creative in following instructions.
Why it's wrong here
Temperature affects randomness and creativity, not instruction-following. A higher temperature would make outputs more varied and potentially less accurate, which is counterproductive for summarization. It does not address the problem of the model ignoring earlier instructions in long contexts.
- ✗
Split the article into smaller chunks and summarize each chunk separately, then concatenate the summaries.
Why it's wrong here
Chunking and summarizing separately can lose cross-chunk context and may not preserve overall coherence. While it can handle long inputs, it does not directly improve instruction adherence for a single summarization task and may increase cost and complexity. It is not the most effective prompt engineering fix for this scenario.
- ✗
Increase the maxTokenCount parameter to allow the model to process more of the article.
Why it's wrong here
The maxTokenCount parameter controls the maximum number of tokens in the generated response, not the input context. Increasing it would allow longer outputs but would not help the model follow instructions embedded earlier in a long input. The issue is attention distribution, not output length.
- ✓
Move the instructions to the end of the prompt and repeat them after the article text.
Why this is correct
Placing instructions at the end of the prompt, after the long context, leverages the model's tendency to pay more attention to recent tokens. Repeating key instructions after the article text reinforces them, improving adherence without changing the model or adding significant cost. This is a known prompt engineering technique for long-context scenarios.
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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 Amazon Web Services exam blueprint
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