September 3, 2024 4 min read

Chain-of-Thought Prompting: Improving AI Reasoning Today

OpenAI Chain-of-Thought Prompting AI GPT-4o

Chain-of-thought (CoT) prompting has been a powerful technique for improving LLM performance on complex tasks. Let’s explore how to use it effectively and what the future might hold.

Traditional Chain-of-Thought Prompting

We can significantly improve reasoning by encouraging step-by-step thinking:

from openai import OpenAI

client = OpenAI()

# Traditional CoT with GPT-4o
cot_prompt = """
Solve this problem step by step:

A store sells apples for $2 each and oranges for $3 each.
If someone buys 5 fruits and spends exactly $12,
how many of each fruit did they buy?

Let's think through this step by step:
1. First, identify the variables
2. Set up the equations
3. Solve the system
4. Verify the answer
"""

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": cot_prompt}]
)

Why Chain-of-Thought Works

Without explicit prompting, models tend to jump to answers. With CoT, the model:

Breaks down the problem into manageable steps
Shows intermediate work that can be verified
Self-corrects when intermediate steps reveal errors
Produces more reliable final answers

Structured CoT Templates

def solve_with_structured_cot(problem: str) -> str:
    """Use structured CoT for better results"""

    prompt = f"""
Problem: {problem}

Please solve using this structure:
1. **Understanding**: What are we being asked?
2. **Given Information**: What facts do we have?
3. **Approach**: What method will we use?
4. **Solution**: Work through the steps
5. **Verification**: Check the answer

Think carefully at each step.
"""

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )

    return response.choices[0].message.content

Comparing Direct vs CoT Prompting

import time

def compare_approaches(problem: str) -> dict:
    """Compare direct prompting vs CoT"""

    # Direct approach
    start = time.time()
    direct_response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": problem}]
    )
    direct_time = time.time() - start

    # CoT approach
    start = time.time()
    cot_response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"Solve step by step:\n{problem}\n\nLet's think through this carefully:"
        }]
    )
    cot_time = time.time() - start

    return {
        "direct": {
            "response": direct_response.choices[0].message.content,
            "time": direct_time,
            "tokens": direct_response.usage.total_tokens
        },
        "cot": {
            "response": cot_response.choices[0].message.content,
            "time": cot_time,
            "tokens": cot_response.usage.total_tokens
        }
    }

Advanced CoT Techniques

Self-Consistency

Generate multiple reasoning paths and select the most common answer:

def solve_with_self_consistency(problem: str, num_samples: int = 5) -> dict:
    """Use self-consistency for more reliable answers"""

    responses = []
    for _ in range(num_samples):
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[{
                "role": "user",
                "content": f"Solve step by step: {problem}"
            }],
            temperature=0.7  # Add variation
        )
        responses.append(response.choices[0].message.content)

    # Extract and compare final answers
    return {
        "responses": responses,
        "final_answer": extract_consensus(responses)
    }

def extract_consensus(responses: list) -> str:
    """Extract the most common final answer"""
    # Implementation depends on answer format
    pass

Verification Chain

Solve, then verify separately:

def solve_and_verify(problem: str) -> dict:
    """Solve with verification step"""

    # Solve
    solution = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"Solve step by step: {problem}"
        }]
    ).choices[0].message.content

    # Verify
    verification = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"""
Problem: {problem}

Proposed solution:
{solution}

Please verify this solution:
1. Check each step for errors
2. Verify the final answer is correct
3. Note any issues found
"""
        }]
    ).choices[0].message.content

    return {
        "solution": solution,
        "verification": verification
    }

When to Use CoT

Good Use Cases

cot_recommended = [
    "Mathematical word problems",
    "Multi-step logical reasoning",
    "Code debugging",
    "Complex analysis tasks",
    "Planning and strategy"
]

Not Always Necessary

skip_cot = [
    "Simple factual questions",
    "Creative writing",
    "Translation tasks",
    "Straightforward classification"
]

Decision Framework

def choose_prompting_approach(task_type: str, accuracy_requirement: str) -> str:
    """
    Decide whether to use CoT prompting
    """
    if task_type in ["reasoning", "math", "analysis"]:
        if accuracy_requirement == "high":
            return "cot_with_verification"
        else:
            return "basic_cot"
    else:
        return "direct"

Looking Ahead: Native Reasoning

The AI research community is exploring models that reason internally without explicit prompting. The idea is:

Current: Input -> Prompt encourages reasoning -> Output
Future: Input -> Native internal reasoning -> Output

OpenAI and other labs are likely working on models with built-in reasoning capabilities. When these arrive, they may reduce the need for explicit CoT prompting.

For now, CoT remains one of our best tools for improving AI reasoning.

Best Practices

Be explicit about wanting step-by-step reasoning
Provide structure for the reasoning process
Use verification for high-stakes problems
Try self-consistency for difficult problems
Match approach to task - not everything needs CoT

Conclusion

Chain-of-thought prompting significantly improves reasoning quality in current models. While future models may reason natively, understanding CoT principles helps you:

Get better results from today’s models
Understand how AI reasoning works
Debug reasoning failures
Build flexible architectures for the future