Chain-of-Thought Prompting

What is Chain-of-Thought Prompting?

Chain-of-Thought (CoT) prompting is a method where you guide a language model to solve a problem by thinking step-by-step, instead of jumping directly to an answer. This improves performance on tasks that require reasoning, such as math, logic, and multi-step decision making.

---

Why Use It?

• Helps the model reason more clearly
• Improves accuracy for complex problems
• Makes the process transparent and easier to debug

---

How It Works in Zedflows

In Zedflows, you can implement CoT prompting using:

• An Input Node for the question
• One or more LLM Nodes to perform the step-by-step reasoning
• Optional Parallel Nodes to try different reasoning styles
• A Critique Node to evaluate the answers and select the best one

---

Basic Example

Input Question:

If Alice has 3 apples and buys 2 more, how many apples does she have?

Prompt to LLM:

Question: If Alice has 3 apples and buys 2 more, how many apples does she have?

Think step-by-step to find the answer.

LLM Output:

Alice starts with 3 apples.  
She buys 2 more apples.  
3 + 2 = 5  
Answer: 5

---

Advanced Example with Parallel Reasoning

Input Question:

John has twice as many apples as Sarah. Together they have 18 apples.  
How many apples does each person have?

Parallel LLMs (different roles):

• LLM A (Math Teacher):

  Let Sarah have x apples.  
  Then John has 2x apples.  
  Together: x + 2x = 18  
  3x = 18  
  x = 6 (Sarah), 2x = 12 (John)  
  

• LLM B (Logic Solver):

  If Sarah has 6 apples, John would have 12.  
  6 + 12 = 18, so the numbers work.  
  Final Answer: Sarah has 6, John has 12.
  

Critique LLM:

All answers are correct and consistent.  
LLM A provided the full equation-based breakdown.  
LLM A's response is more thorough.  
Selected Answer: Sarah has 6 apples, John has 12 apples.

---

Summary

Chain-of-Thought prompting makes complex tasks easier for language models by forcing them to think before answering. With Zedflows, you can expand this method using parallel nodes and self-critiquing workflows for even better results.

---