NCP-GENL Prompt Engineering Practice Question
When building an NVIDIA NeMo LLM application for automated document review, which THREE of the following prompt design choices are critical for ensuring high-quality output? (Select exactly THREE)
⚠ Common exam trap
Candidates often neglect the importance of machine-readable output formats, focusing only on the content of the response while ignoring the necessity of programmatic integration for automated document review workflows.
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 standardized delimiters to isolate user-provided documents.
Effective document review requires accuracy, traceability, and consistency. Using clear delimitation prevents data corruption, requiring structured output (like JSON) allows for downstream programmatic integration, and chain-of-thought prompts ensure the model validates its conclusions against the text. These choices together create a robust, production-ready pipeline that minimizes errors and provides the necessary structure for automated workflows in enterprise environments.
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 standardized delimiters to isolate user-provided documents.
Why this is correct
Properly isolating document segments prevents the model from conflating the input data with its own internal knowledge or instructions. This clarity is essential for document review applications where accuracy is paramount, as it ensures the model is specifically analyzing the provided text rather than hallucinating based on external training.
- ✗
Include instructions that prioritize speed over accuracy.
Why it's wrong here
In document review, accuracy must always be the priority. Encouraging the model to sacrifice accuracy for speed will lead to missed errors and incorrect conclusions. This directly contradicts the purpose of a review application, which is to ensure compliance and technical correctness in sensitive documentation or data.
- ✓
Require the output to be in a machine-readable format like JSON.
Why this is correct
Outputting in JSON is critical for programmatic integration. It allows the review results to be easily parsed and processed by other systems in the pipeline, such as automated reporting tools or database loggers. This creates a predictable interface between the LLM's natural language processing and the enterprise's software architecture.
- ✓
Instruct the model to perform a chain-of-thought validation of its findings.
Why this is correct
Requiring the model to explicitly state its reasoning before giving a final conclusion forces it to 'check its work' against the provided document. This internal verification step drastically reduces errors in logic and interpretation, which is vital when performing complex tasks like legal or technical document review.
- ✗
Use a high temperature setting to ensure diverse review perspectives.
Why it's wrong here
High temperature causes inconsistent results, which is unacceptable for document review. The review process must be deterministic and reproducible. A high temperature would lead to different findings for the exact same document, destroying the credibility of the automated review pipeline and creating legal or quality control risks.
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JA
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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