NCP-GENL Prompt Engineering Practice Question
A team is using an NVIDIA NIM-hosted Llama model to generate product descriptions from a list of technical specifications. The descriptions sometimes omit key specifications or include invented features. The team wants to improve reliability without changing the model. Which prompt engineering change is most likely to reduce these errors?
⚠ Common exam trap
The trap here is thinking that lowering temperature or increasing max_tokens will fix content errors, when the real solution is to explicitly instruct the model and show it an example of the desired output.
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
✓
Add a system prompt that instructs the model to act as a technical writer and to include every specification exactly as given, and provide a few-shot example of a correct description.
To reduce omissions and inventions without changing the model, the most effective prompt engineering approach is to provide explicit instructions and few-shot examples. A system prompt that defines the role and constraints, combined with an example that demonstrates including all specifications and avoiding fabrication, guides the model to produce more reliable outputs. Other parameter changes or chain-of-thought do not directly address content fidelity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a system prompt that instructs the model to act as a technical writer and to include every specification exactly as given, and provide a few-shot example of a correct description.
Why this is correct
A system prompt sets the model's role and constraints, and few-shot examples demonstrate the desired output format and level of detail. By showing a correct description that includes all specifications without additions, the model is more likely to follow that pattern. This directly addresses both omission and invention by providing clear instructions and a concrete example, without retraining the model.
- ✗
Increase the max_tokens parameter to allow the model to generate longer descriptions, ensuring all specifications are covered.
Why it's wrong here
Increasing max_tokens only allows longer outputs; it does not ensure that the model includes all specifications or avoids inventing features. The model might still omit details or add fabricated content even with more tokens. The problem is about content fidelity, not length, so this change is unlikely to solve the issue and could even encourage rambling.
- ✗
Use a chain-of-thought prompt that asks the model to list each specification and then write the description.
Why it's wrong here
Chain-of-thought can help with reasoning tasks, but for a generation task like writing a product description, it may not directly prevent omissions or inventions. The model might still fail to include all specifications in the final output even if it lists them first, and it could still invent features. A system prompt with few-shot examples more directly shapes the output content and style.
- ✗
Set the temperature to 0.0 to make the model deterministic and reduce creativity.
Why it's wrong here
Lowering temperature reduces randomness, which can help with consistency, but it does not guarantee that all specifications are included or that invented features are eliminated. A deterministic model can still consistently omit a specification or consistently hallucinate a feature. The core issue requires explicit instructions and examples to guide the model's content selection, not just reduced randomness.
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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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