NCP-GENL Prompt Engineering Practice Question
A technical support team is building a chatbot using an NVIDIA NIM microservice. The chatbot must answer questions about a specific product's warranty policy. The team wants to ensure the model's responses are grounded in the official warranty document, which is 50 pages long, and avoid inventing policy details. Which prompt engineering approach is most effective for this scenario?
⚠ Common exam trap
The trap here is assuming that fine-tuning is necessary to teach the model a document, when RAG with prompt context is often simpler, more accurate, and easier to update.
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 a retrieval-augmented generation (RAG) pipeline to fetch the most relevant warranty sections and include them in the prompt as context.
Retrieval-augmented generation (RAG) is the most effective way to ground responses in a specific document. It retrieves relevant sections and includes them in the prompt, ensuring the model's answer is based on factual content rather than pre-trained memory. This reduces hallucinations and handles documents that exceed the context window. Other methods either risk hallucination, are inefficient, or lack flexibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ask the model to 'think step by step' and reason about the warranty policy from its pre-trained knowledge.
Why it's wrong here
The model's pre-trained knowledge likely does not include the specific warranty policy of this product, so reasoning from memory will lead to invented details. Chain-of-thought does not inject factual grounding; it only structures reasoning. Without the actual document content, the model cannot provide accurate answers. This approach is inappropriate for proprietary or up-to-date information.
- ✓
Use a retrieval-augmented generation (RAG) pipeline to fetch the most relevant warranty sections and include them in the prompt as context.
Why this is correct
RAG retrieves only the pertinent sections of the warranty document and places them in the prompt, providing focused context that the model can use to generate accurate answers. This reduces hallucination because the model is conditioned on specific, relevant text. It also scales to large documents and keeps the prompt within token limits. This is the standard approach for grounding LLM responses in proprietary knowledge bases.
- ✗
Fine-tune the model on the warranty document using NVIDIA NeMo, then use the fine-tuned model without any additional context in the prompt.
Why it's wrong here
Fine-tuning can help the model learn the document's content, but it is costly and may not prevent hallucinations, especially for specific policy details that change. It also requires retraining when the document updates. For a 50-page document, fine-tuning is overkill and less flexible than RAG. Prompt engineering with context is more suitable for this use case.
- ✗
Include the entire warranty document in the system prompt and instruct the model to answer based only on that document.
Why it's wrong here
While including the full document may seem thorough, it can exceed the model's context window or dilute attention, leading to missed details. Also, the model may still hallucinate if the document is not properly segmented or if instructions are not precise. This approach is inefficient and may not scale. A more targeted retrieval of relevant sections is generally more effective for grounding.
About these practice questions
One of 352 original NCP-GENL practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.