Chain-of-thought prompting in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main benefit of chain-of-thought prompting in AI models?

Chain-of-thought prompting helps AI models by:

A Reducing the model's size to improve speed

B Increasing the model's training data size automatically

C Changing the model architecture to add more layers

D Breaking down complex problems into smaller reasoning steps before answering

💡 Hint

Think about how explaining your thinking helps solve hard problems.

Chain-of-thought prompting guides the model to reason step-by-step, improving accuracy on complex tasks.

❓ Predict Output

intermediate

What is the output of this chain-of-thought prompt example?

Given the prompt below, what will the model most likely output?

Prompt Engineering / GenAI

Prompt: "If there are 3 apples and you get 2 more, how many apples do you have? Let's think step-by-step."

A "You start with 3 apples. You get 2 more apples. 3 + 2 = 5 apples."

B "Apples are red and tasty."

C "The answer is 2 apples."

D "You have 3 apples and 2 oranges, so 5 fruits."

💡 Hint

Chain-of-thought prompts encourage stepwise reasoning.

The model explains the addition step-by-step, showing the reasoning before giving the final answer.

❓ Model Choice

advanced

Which model type benefits most from chain-of-thought prompting?

Among these AI model types, which one gains the most accuracy improvement from chain-of-thought prompting?

A Small rule-based expert systems

B Large language models with billions of parameters

C Simple linear regression models

D Basic decision trees with few splits

💡 Hint

Consider which models can generate detailed text explanations.

Large language models can generate multi-step reasoning text, so chain-of-thought prompting improves their performance significantly.

❓ Hyperparameter

advanced

Which hyperparameter setting best supports chain-of-thought prompting in text generation?

When using chain-of-thought prompting, which setting helps the model produce longer, detailed reasoning?

A Setting batch size to 1 for faster inference

B Reducing learning rate to speed up training

C Increasing max token length to allow longer outputs

D Decreasing temperature to zero for deterministic output

💡 Hint

Think about output length and detail in reasoning.

Increasing max token length allows the model to generate longer step-by-step explanations required for chain-of-thought.

❓ Metrics

expert

Which metric best measures improvement from chain-of-thought prompting on reasoning tasks?

You want to evaluate if chain-of-thought prompting improves your model's reasoning. Which metric is most appropriate?

A Accuracy on multi-step reasoning benchmark datasets

B Training loss decrease during fine-tuning

C Inference speed measured in tokens per second

D Model size in number of parameters

💡 Hint

Focus on measuring reasoning correctness, not training or speed.

Accuracy on reasoning benchmarks directly shows if chain-of-thought prompting helps the model solve complex problems correctly.