NVIDIA NCA-GENL Exam - Topic 4 Question 3 Discussion

Actual exam question for NVIDIA's NCA-GENL exam

Question #: 3
Topic #: 4

[Prompt Engineering]

When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

AZero-shot prompting with a generic task description.

BFew-shot prompting with randomly selected examples.

CChain-of-thought prompting with step-by-step reasoning examples.

DRetrieval-augmented generation with external mathematical databases.

Show Suggested Answer

Suggested Answer: C

Chain-of-thought (CoT) prompting is an advanced prompt engineering technique that significantly enhances a large language model's (LLM) performance on complex reasoning tasks, such as multi-step mathematical problems. By including examples that explicitly demonstrate step-by-step reasoning in the prompt, CoT guides the model to break down the problem into intermediate steps, improving accuracy and robustness. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks requiring logical or sequential reasoning, as it leverages the model's ability to mimic structured problem-solving. Research by Wei et al. (2022) demonstrates that CoT outperforms other methods for mathematical reasoning. Option A (zero-shot) is less effective for complex tasks due to lack of guidance. Option B (few-shot with random examples) is suboptimal without structured reasoning. Option D (RAG) is useful for factual queries but less relevant for pure reasoning tasks.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Wei, J., et al. (2022). 'Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.'

by Micah at Jun 15, 2025, 11:51 PM

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Lenny

3 months ago

D seems interesting, but is it really necessary for math problems?

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Miles

4 months ago

Totally agree, chain-of-thought really helps clarify steps!

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Ciara

4 months ago

Wow, I never considered using external databases for this!

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Kristeen

4 months ago

Really? I thought zero-shot could work just as well.

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Julian

4 months ago

I think C is the best choice for complex reasoning tasks.

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Noemi

4 months ago

Retrieval-augmented generation sounds interesting, but I wonder if it’s necessary for just solving math problems.

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Thomasena

5 months ago

I practiced a similar question where few-shot prompting was mentioned, but I think chain-of-thought might be more reliable here.

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Anglea

5 months ago

I’m not sure if zero-shot prompting would be effective for this type of problem. It feels too vague for multi-step math.

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Tijuana

5 months ago

I remember discussing chain-of-thought prompting in class; it seemed to help with complex reasoning tasks.

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Willow

5 months ago

I think I'm going to go with option C, the chain-of-thought prompting. It just seems like the most robust and reliable approach for ensuring the model can tackle these kinds of complex reasoning tasks. Gotta nail down that step-by-step process.

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Theron

6 months ago

I'm a bit unsure about this one. Zero-shot prompting might work, but I'm worried the model won't have enough guidance to handle the complexity. Retrieval-augmented generation could be interesting, but I'm not sure how well it would work in practice.

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Donette

6 months ago

Chain-of-thought prompting with step-by-step examples sounds like the most promising approach to me. Providing the model with a clear structure and reasoning process could really help it tackle these multi-step problems.

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Michal

6 months ago

Hmm, this seems like a tricky one. I'll need to think carefully about the different prompt engineering techniques and how they might impact the model's performance on complex reasoning tasks.

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Jaclyn

8 months ago

Hmm, chain-of-thought prompting, you say? Sounds like the kind of technique that would really put the 'thought' in 'thought-provoking.' I'm in, let's see those step-by-step solutions!

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Sean

8 months ago

Yes, it helps guide the model through the necessary steps to arrive at the correct solution.

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Sarah

8 months ago

Chain-of-thought prompting is definitely a powerful technique for ensuring robust performance.

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Rodolfo

9 months ago

Zero-shot prompting? What is this, amateur hour? If I wanted to half-ass my way through a problem, I'd just use a calculator. Chain-of-thought is the way to go, folks. It's the difference between being a math wizard and a math magician.

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Henriette

8 months ago

I agree, zero-shot prompting seems too risky for complex tasks. Chain-of-thought keeps you on track.

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Derick

8 months ago

Chain-of-thought is definitely the way to go. It's all about that logical progression.

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Kattie

9 months ago

Retrieval-augmented generation, huh? Sounds fancy, but I bet it's just an excuse to have the model cheat by looking up the answers. I'm going with chain-of-thought, the classic way to go.

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Lili

8 months ago

Chain-of-thought prompting with step-by-step reasoning examples is definitely a solid choice.

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Shawna

8 months ago

I think few-shot prompting with randomly selected examples could also be effective.

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Casandra

9 months ago

I agree, retrieval-augmented generation does sound like cheating.

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Brett

9 months ago

I prefer retrieval-augmented generation with external mathematical databases, as it provides more accurate information.

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Curtis

9 months ago

I agree with Ivory, because it helps the model understand the reasoning process better.

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Ivory

9 months ago

I think chain-of-thought prompting with step-by-step reasoning examples is the most effective.

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Danica

9 months ago

Few-shot prompting? Nah, that's just throwing a bunch of random examples at the wall and hoping something sticks. Give me that good ol' chain-of-thought approach any day!

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Lino

10 months ago

Hmm, this is a tough one. I'd say chain-of-thought prompting seems like the most robust approach. Seeing the step-by-step reasoning really helps the model understand the problem-solving process.

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