3 Prompting Techniques for Reasoning in LLMs

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Large Language Models (LLMs) like GPT-4 can produce highly accurate and logical responses when prompted correctly. Below are three powerful prompting techniques to enhance reasoning in LLMs, along with practical implementations.

1️⃣ Chain of Thought (CoT)

Instead of directly asking for an answer, guide the LLM to reason step-by-step.

Example

Q: I moved 10m North, then 30m East, then 50m West, and finally, 60m North. How far am I from the initial location? 
Let's think step by step:

Expected Output:

1. Initial position: (0, 0) 
2. Move 10m North → (0, 10) 
3. Move 30m East → (30, 10) 
4. Move 50m West → (-20, 10) 
5. Move 60m North → (-20, 70) 
6. Distance from start: √(20² + 70²) ≈ 72.8m

You Should Know:

  • Use `python` to verify calculations: 
    import math 
x, y = -20, 70 
distance = math.sqrt(x2 + y2) 
print(distance)  Output: 72.801
  • In Linux, calculate distance using bc: 
    echo "scale=2; sqrt((-20)^2 + 70^2)" | bc -l

2️⃣ Self-Consistency

Generate multiple reasoning paths and pick the most frequent answer.

Example

Q: If a train travels 300 km in 2 hours, what is its speed? 
Let's think step by step:

Run multiple times and aggregate answers.

You Should Know:

  • Use Bash scripting to automate multiple LLM calls: 
    for i in {1..5}; do 
curl -X POST https://api.openai.com/v1/chat/completions \ 
-H "Authorization: Bearer YOUR_API_KEY" \ 
-H "Content-Type: application/json" \ 
-d '{"model":"gpt-4","messages":[{"role":"user","content":"Q: If a train travels 300 km in 2 hours, what is its speed? Let\'s think step by step:"}]}' 
done
  • Filter the most common answer using sort | uniq -c.

3️⃣ Tree of Thoughts (ToT)

Explore multiple reasoning branches and select the best path.

Example

Q: Solve for x: 3x + 5 = 20 
Possible steps: 
1. Subtract 5 from both sides 
2. Divide by 3

You Should Know:

  • Python verification: 
    x = (20 - 5) / 3 
print(x)  Output: 5.0
  • Windows CMD math: 
    set /a x=(20-5)/3 
echo %x%
  • Linux alternative: 
    echo $(( (20 - 5) / 3 ))

What Undercode Say

  • Chain of Thought (CoT) improves accuracy by forcing structured reasoning.
  • Self-Consistency reduces errors via majority voting.
  • Tree of Thoughts (ToT) is computationally heavy but yields the best logical paths.

Practical Commands Recap:

  • Python: math.sqrt(), equation solving
  • Bash: bc, `curl` API calls, `sort | uniq -c`
  • Windows CMD: `set /a` for arithmetic

Expected Output:

A well-reasoned, step-by-step solution with computational verification.

Prediction

Future LLMs will automate these techniques, making reasoning prompts even more efficient.

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