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.

Practice

1. What is the main purpose of chain-of-thought prompting in AI?

easy

A. To increase the randomness of AI answers

B. To make AI respond faster

C. To reduce the size of the AI model

D. To help AI explain its reasoning step-by-step

Solution

1. Step 1: Understand the concept of chain-of-thought prompting

   It is designed to guide AI to explain its reasoning in steps rather than giving a direct answer.

2. Step 2: Identify the main goal

   The goal is to improve clarity and accuracy by showing the reasoning process.

3. Final Answer:

   To help AI explain its reasoning step-by-step -> Option D

4. Quick Check:

   Chain-of-thought = step-by-step reasoning [OK]

Hint: Think: Does it explain or just answer? Explanation means chain-of-thought [OK]

Common Mistakes:

• Confusing speed with reasoning clarity
• Thinking it reduces model size
• Assuming it increases randomness

2. Which of the following is the correct way to start a chain-of-thought prompt?

easy

A. "Let's think step-by-step."

B. "Answer quickly without explanation."

C. "Give me a random fact."

D. "Explain your answer in one word."

Solution

1. Step 1: Identify phrases that encourage stepwise reasoning

   "Let's think step-by-step" clearly asks for a stepwise explanation.

2. Step 2: Eliminate options that do not prompt reasoning

   The options asking for a one-word answer, quick response without explanation, or a random fact do not encourage detailed reasoning.

3. Final Answer:

   "Let's think step-by-step." -> Option A

4. Quick Check:

   Prompt that guides stepwise thinking = "Let's think step-by-step." [OK]

Hint: Look for prompts that say 'step-by-step' or 'explain' [OK]

Common Mistakes:

• Choosing prompts that ask for short or random answers
• Ignoring the need for explanation
• Confusing speed with reasoning

3. Given this prompt: "If you have 3 apples and get 2 more, how many apples do you have? Let's think step-by-step."
What is the expected output from the AI?

medium

A. "You have 2 apples."

B. "5 apples"

C. "3 plus 2 equals 5, so you have 5 apples."

D. "Apples are fruits."

Solution

1. Step 1: Understand the prompt asks for step-by-step reasoning

   The phrase "Let's think step-by-step" asks the AI to explain the calculation process.

2. Step 2: Identify the output that shows reasoning

   "3 plus 2 equals 5, so you have 5 apples." explains the addition step and then gives the answer, matching the prompt's request.

3. Final Answer:

   "3 plus 2 equals 5, so you have 5 apples." -> Option C

4. Quick Check:

   Step-by-step prompt = explanation + answer [OK]

Hint: Look for answers that explain before concluding [OK]

Common Mistakes:

• Choosing only the final answer without explanation
• Picking unrelated or incomplete answers
• Ignoring the step-by-step request

4. You wrote this prompt: "Calculate 10 minus 4. Think step-by-step."
The AI responds with just "6". What is the likely problem?

medium

A. The question is too hard for AI

B. The prompt does not clearly ask for step-by-step explanation

C. The AI model is broken

D. The prompt is too long

Solution

1. Step 1: Analyze the prompt wording

   The prompt says "Think step-by-step" but does not clearly say "Let's think step-by-step" or "Explain step-by-step."

2. Step 2: Understand AI needs clear instructions

   Without a clear phrase like "Let's think step-by-step," AI may skip explanation and give a direct answer.

3. Final Answer:

   The prompt does not clearly ask for step-by-step explanation -> Option B

4. Quick Check:

   Clear prompt needed for explanation [OK]

Hint: Use exact phrases like 'Let's think step-by-step' for explanations [OK]

Common Mistakes:

• Blaming AI model instead of prompt clarity
• Assuming question difficulty causes no explanation
• Thinking prompt length affects reasoning

5. You want the AI to solve this complex problem: "If a train travels 60 miles in 1 hour and then 90 miles in 1.5 hours, what is the average speed? Let's think step-by-step."
Which chain-of-thought prompt addition will best improve the AI's accuracy?

hard

A. "Explain each calculation clearly before giving the final answer."

B. "Just give the final number quickly."

C. "Ignore the question and talk about trains."

D. "Answer with a random speed value."

Solution

1. Step 1: Recognize the need for detailed reasoning in complex problems

   Complex problems benefit from clear stepwise explanations to avoid mistakes.

2. Step 2: Identify the prompt that encourages detailed calculation explanation

   "Explain each calculation clearly before giving the final answer." explicitly asks for clear explanation before the final answer, improving accuracy.

3. Final Answer:

   "Explain each calculation clearly before giving the final answer." -> Option A

4. Quick Check:

   Detailed explanation improves accuracy [OK]

Hint: Ask AI to explain calculations clearly for complex problems [OK]

Common Mistakes:

• Choosing prompts that skip explanation
• Ignoring the problem complexity
• Selecting irrelevant or random answer options