1. Introduction

Chain-of-Thought prompting is a prompting technique used to improve performance on tasks that require multiple reasoning steps. Instead of treating a problem as a single question, the task is structured so that the model can work through intermediate reasoning before producing the final result. It is especially useful for tasks involving:

Mathematics
Logic
Multi-step calculations
Programming problems
Planning
Complex decision-making

The two main ideas are:

Few-Shot Prompting
        +
Step-by-Step Reasoning
        =
Chain-of-Thought Prompting

2. Few-Shot Prompting

Definition

Few-shot prompting provides the model with a small number of examples before giving it the actual task. The examples demonstrate:

What the task looks like
How the input should be interpreted
What type of output is expected
What reasoning pattern or solution approach is useful

Basic Example

Question:
What is 2 + 3?

Answer:
5

Question:
What is 10 + 7?

Answer:
17

Question:
What is 8 + 6?

Answer:

The model uses the examples to understand the task.

3. Few-Shot Prompting for More Complex Tasks

Few-shot examples can demonstrate how to solve a category of problems.

Example 1:

Question:
A box contains 5 apples. 2 apples are removed.
How many apples remain?

Answer:
5 - 2 = 3

Final Answer: 3


Example 2:

Question:
A store has 10 books and receives 4 more books.
How many books are there now?

Answer:
10 + 4 = 14

Final Answer: 14


Question:
A library has 20 books and gives away 6 books.
How many books remain?

Answer:

The examples guide the model toward the expected problem-solving pattern.

4. Step-by-Step Reasoning

Definition

Some problems require multiple steps before reaching a final answer. For these tasks, a useful prompt can ask the model to:

Identify the important information.
Break the problem into smaller parts.
Perform the required operations.
Check the result.
Return the final answer.

Example

Solve the following problem carefully.

Identify the required information.
Work through the necessary steps.
Check the calculation.
Then provide the final answer with a concise explanation.

Question:
A person buys 3 notebooks for ₹25 each.
What is the total cost?

The calculation is:

3 × 25 = 75

Final answer:

₹75

5. Combining Few-Shot and Step-by-Step Reasoning

Chain-of-Thought prompting often combines examples with a structured reasoning approach.

Example Prompt

Solve the problems using the same approach as the examples.

Example:

Question:
A train travels 60 km per hour for 2 hours.
How far does it travel?

Solution:
Distance = Speed × Time
Distance = 60 × 2
Distance = 120 km

Final Answer: 120 km


Now solve:

Question:
A car travels 80 km per hour for 3 hours.
How far does it travel?

Provide the final answer with a concise explanation.

The example demonstrates the structure and approach expected for the new problem.

6. Zero-Shot vs Few-Shot Chain-of-Thought

Zero-Shot

The model receives instructions but no examples.

Solve the following problem carefully.
Break it into the necessary steps.
Verify the result.

Question:
If 5 workers complete a task in 10 days,
how many worker-days are required?

The model must determine the reasoning pattern from the instruction.

Few-Shot

The model receives examples before the actual problem.

Example:

Question:
If 4 workers work for 5 days,
how many worker-days are required?

Solution:
Worker-days = Workers × Days
Worker-days = 4 × 5
Worker-days = 20

Final Answer: 20


Question:
If 5 workers work for 10 days,
how many worker-days are required?

Answer:

Few-shot prompting provides an example of the expected reasoning pattern.

7. General Chain-of-Thought Prompt Template

A reusable template is:

You are an expert {role}.

Solve the following problem carefully.

Requirements:
1. Identify the important information.
2. Break the problem into smaller steps.
3. Apply the appropriate method.
4. Check the result for errors.
5. Provide the final answer with a concise explanation.

Problem:
{problem}

8. Few-Shot Chain-of-Thought Template

You are an expert problem solver.

Solve problems using the approach demonstrated below.

Example 1:

Problem:
{example_problem_1}

Solution:
{example_solution_1}

Final Answer:
{example_answer_1}

Example 2:

Problem:
{example_problem_2}

Solution:
{example_solution_2}

Final Answer:
{example_answer_2}

Now solve:

Problem:
{new_problem}

Provide a concise final explanation and answer.

9. When to Use Chain-of-Thought Prompting

It is useful when the task involves:

Multi-Step Problems

Analyze the requirements, solve the task, and verify the result.

Mathematical Problems

Solve the calculation carefully and check the final result.

Programming Problems

Analyze the bug, identify the root cause, propose a fix,
and verify that the solution satisfies the requirements.

Data Analysis

Analyze the data, identify important patterns,
check for inconsistencies, and provide conclusions.

Planning

Break the objective into smaller tasks,
identify dependencies, and create a step-by-step plan.

10. Reasoning and Verification

For complex tasks, it is often useful to separate solving from checking.

Example

Task:
Solve the problem.

Verification:
Check that:
- All requirements are addressed.
- The calculations are correct.
- No assumptions conflict with the provided information.

Return:
A concise explanation and the final answer.

This creates a simple workflow:

Understand Task
      ↓
Solve Problem
      ↓
Check Result
      ↓
Correct Errors
      ↓
Return Final Answer

11. Best Practices

1. Use Clear Examples

Few-shot examples should be:

Relevant to the actual task
Correct
Consistent
Simple enough to demonstrate the pattern

2. Use Similar Examples

If the actual task is mathematical, use mathematical examples. If the actual task is code generation, use code examples. Poorly matched examples can confuse the model.

3. Ask for Verification

For important tasks, add verification criteria.

Before giving the final answer, check:

- Does the solution answer the question?
- Are calculations correct?
- Are all requirements satisfied?

4. Avoid Unnecessary Complexity

Not every task requires detailed reasoning. Simple tasks often work well with direct prompting.

Simple Task

Translate this sentence into French.

Complex Task

Analyze the problem, identify the relevant constraints,
work through the solution, verify it, and provide the final answer.

Use additional reasoning guidance only when the task benefits from it.

12. Chain-of-Thought vs Direct Prompting

Feature	Direct Prompting	Chain-of-Thought Prompting
Task complexity	Simple tasks	Multi-step tasks
Examples	Optional	Often useful
Intermediate reasoning	Usually not emphasized	Structured problem-solving approach
Verification	Optional	Often included
Best for	Simple questions and generation	Complex reasoning and planning

13. Important Limitation

A model’s reasoning process is not guaranteed to be correct simply because it produces a step-by-step explanation. A response can still contain:

Incorrect assumptions
Calculation errors
Missing information
Logical mistakes

Therefore, for important tasks, use external verification when possible. For example:

Use a calculator for important calculations
Run generated code
Validate JSON with a parser
Check results against source data
Test software with unit tests

14. Complete Example

Prompt

You are an expert problem solver.

Solve the problem carefully.

Example:

Question:
A shop sells 4 pens for ₹10 each.

Solution:
Total Cost = Number of Pens × Cost per Pen
Total Cost = 4 × 10
Total Cost = ₹40

Final Answer: ₹40


Now solve:

Question:
A student buys 6 notebooks for ₹35 each.

Requirements:
- Identify the required calculation.
- Solve the problem.
- Check the result.
- Provide a concise explanation.

Expected Solution

Total Cost = Number of Notebooks × Cost per Notebook

Total Cost = 6 × 35

Total Cost = ₹210

Final Answer: ₹210

15. Key Takeaways

Topic	Explanation
Chain-of-Thought Prompting	A technique for improving performance on multi-step reasoning tasks
Few-Shot Prompting	Provide examples that demonstrate the task or expected pattern
Step-by-Step Reasoning	Break complex tasks into smaller logical stages
Reasoning Guidance	Instruct the model to analyze requirements and solve systematically
Verification	Check whether the result satisfies requirements and contains errors
Zero-Shot Approach	Give instructions without examples
Few-Shot Approach	Give examples before the actual task
Best Use Cases	Mathematics, logic, programming, planning, and complex analysis
Limitation	Step-by-step explanations do not guarantee correctness

Simple Formula

Complex Problem
      ↓
Relevant Examples
      +
Clear Reasoning Instructions
      +
Verification
      ↓
More Reliable Final Answer

​1. Introduction

​2. Few-Shot Prompting

​Definition

​Basic Example

​3. Few-Shot Prompting for More Complex Tasks

​4. Step-by-Step Reasoning

​Definition

​Example

​5. Combining Few-Shot and Step-by-Step Reasoning

​Example Prompt

​6. Zero-Shot vs Few-Shot Chain-of-Thought

​Zero-Shot

​Few-Shot

​7. General Chain-of-Thought Prompt Template

​8. Few-Shot Chain-of-Thought Template

​9. When to Use Chain-of-Thought Prompting

​Multi-Step Problems

​Mathematical Problems

​Programming Problems

​Data Analysis

​Planning

​10. Reasoning and Verification

​Example

​11. Best Practices

​1. Use Clear Examples

​2. Use Similar Examples

​3. Ask for Verification

​4. Avoid Unnecessary Complexity

​Simple Task

​Complex Task

​12. Chain-of-Thought vs Direct Prompting

​13. Important Limitation

​14. Complete Example

​Prompt

​Expected Solution

​15. Key Takeaways

​Simple Formula

1. Introduction

2. Few-Shot Prompting

Definition

Basic Example

3. Few-Shot Prompting for More Complex Tasks

4. Step-by-Step Reasoning

Definition

Example

5. Combining Few-Shot and Step-by-Step Reasoning

Example Prompt

6. Zero-Shot vs Few-Shot Chain-of-Thought

Zero-Shot

Few-Shot

7. General Chain-of-Thought Prompt Template

8. Few-Shot Chain-of-Thought Template

9. When to Use Chain-of-Thought Prompting

Multi-Step Problems

Mathematical Problems

Programming Problems

Data Analysis

Planning

10. Reasoning and Verification

Example

11. Best Practices

1. Use Clear Examples

2. Use Similar Examples

3. Ask for Verification

4. Avoid Unnecessary Complexity

Simple Task

Complex Task

12. Chain-of-Thought vs Direct Prompting

13. Important Limitation

14. Complete Example

Prompt

Expected Solution

15. Key Takeaways

Simple Formula