NCP-GENL Prompt Engineering Practice Question
Which prompt engineering strategy helps the model maintain focus when processing an extremely long document within a single context window?
⚠ Common exam trap
Many candidates suggest increasing the context window size further, overlooking the 'Lost in the Middle' phenomenon where long documents are ignored.
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
✓
Instruct the model to analyze the document in sections and summarize each.
Long context windows can lead to the 'Lost in the Middle' phenomenon, where the model performs better on the beginning and end of the document but ignores the middle. Using 'attention-focusing' instructions or prompting the model to summarize segments before synthesizing a final answer helps maintain performance across the entire document. This is critical for NVIDIA engineers working with large-scale technical whitepapers and documentation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Always increase the model's temperature to 1.0.
Why it's wrong here
Increasing the temperature does not help with context management; it only increases randomness. In a long-context task, you need maximum stability and focus. Adding noise through a high temperature will only exacerbate the issue of the model losing track of the document's central theme and structure.
- ✓
Instruct the model to analyze the document in sections and summarize each.
Why this is correct
Breaking down a large document into segments for analysis ensures that the model provides equal attention to all parts. By summarizing each section before synthesis, you force the model to retain the key information from throughout the text, effectively mitigating the risk of ignoring information in the middle.
- ✗
Remove all system-level instructions to save tokens.
Why it's wrong here
System-level instructions are vital for defining the behavior and constraints of the model. Removing them will lead to model drift, where the output becomes unfocused and potentially deviates from the desired task. Efficiency should never come at the cost of the fundamental guidance the model requires to function.
- ✗
Limit the context window size to 1024 tokens.
Why it's wrong here
Artificially limiting the context window size will simply truncate the document, making it impossible to analyze the entire text. If your document is long, you need the full context window available to process it, not a reduced one that throws away the majority of the relevant data provided.
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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 NVIDIA exam blueprint
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