Beginner Explanation
Imagine you’re trying to solve a puzzle, like a jigsaw. Instead of just randomly placing pieces, you think step by step about where each piece might fit. Chain-of-Thought reasoning is like that for computers. It helps them figure things out by breaking down a problem into smaller, manageable steps, just like you would do with the puzzle pieces. This way, they can understand what they’re doing better and explain their reasoning, just like you could explain how you solved the puzzle.Technical Explanation
Chain-of-Thought (CoT) reasoning is a technique in AI that enhances model performance by generating intermediate reasoning steps before arriving at a conclusion. This structured approach can improve the model’s explainability and accuracy in complex tasks. For instance, in natural language processing, a model may be prompted to solve a math problem by first breaking it down into smaller steps. Here’s a simple Python example using the OpenAI API: “`python import openai openai.api_key = ‘YOUR_API_KEY’ prompt = ‘What is 23 + 45? Please show your reasoning step by step.’ response = openai.ChatCompletion.create( model=’gpt-3.5-turbo’, messages=[{‘role’: ‘user’, ‘content’: prompt}] ) print(response[‘choices’][0][‘message’][‘content’]) “` This code prompts the model to provide a step-by-step reasoning process for the addition problem, illustrating CoT reasoning in action.Academic Context
Chain-of-Thought (CoT) reasoning has gained attention in AI research for its potential to improve model interpretability and performance. The foundational work by Wei et al. (2022) in ‘Chain of Thought Prompting Elicits Reasoning in Large Language Models’ demonstrated that prompting language models to generate intermediate reasoning steps significantly enhances their problem-solving capabilities. Mathematically, CoT can be framed within the context of probabilistic reasoning, where the model learns to maximize the likelihood of the correct answer given a sequence of reasoning steps. This approach aligns with theories in cognitive science regarding human reasoning processes, suggesting that structured reasoning can lead to better decision-making outcomes.Code Examples
Example 1:
import openai
openai.api_key = 'YOUR_API_KEY'
prompt = 'What is 23 + 45? Please show your reasoning step by step.'
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=[{'role': 'user', 'content': prompt}]
)
print(response['choices'][0]['message']['content'])Example 2:
model='gpt-3.5-turbo',
messages=[{'role': 'user', 'content': prompt}]Example 3:
import openai
openai.api_key = 'YOUR_API_KEY'
prompt = 'What is 23 + 45? Please show your reasoning step by step.'View Source: https://arxiv.org/abs/2511.16635v1