Chain of Thought Reasoning

Tutorial 4

Jan Kirenz

Chain of Thought Reasoning

Chain-of-thought (CoT) prompting enables complex reasoning capabilities through intermediate reasoning steps.

You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before responding.

Setup

Python

import os
import openai
import sys
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

openai.api_key = os.environ['OPENAI_API_KEY']

Helper function

def get_completion_from_messages(messages,
                                 model="gpt-3.5-turbo",
                                 temperature=0, max_tokens=500):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature,
        max_tokens=max_tokens,
    )
    return response.choices[0].message["content"]

Chain-of-Thought Prompting 1

System message

delimiter = "##"

system_message = f"""
Follow these steps to answer the customer queries.
The customer query will be delimited with four hashtags,\
i.e. {delimiter}. 

Step 1: {delimiter} First decide whether the user is \
asking a question about a specific product or products. \
Product cateogry doesn't count. 

Step 2: {delimiter} If the user is asking about \
specific products, identify whether \
the products are in the following list.
All available products: 
1. Product: TechPro Ultrabook
   Category: Computers and Laptops
   Brand: TechPro
   Model Number: TP-UB100
   Warranty: 1 year
   Rating: 4.5
   Features: 13.3-inch display, 8GB RAM, 256GB SSD, Intel Core i5 processor
   Description: A sleek and lightweight ultrabook for everyday use.
   Price: $799.99

2. Product: BlueWave Gaming Laptop
   Category: Computers and Laptops
   Brand: BlueWave
   Model Number: BW-GL200
   Warranty: 2 years
   Rating: 4.7
   Features: 15.6-inch display, 16GB RAM, 512GB SSD, NVIDIA GeForce RTX 3060
   Description: A high-performance gaming laptop for an immersive experience.
   Price: $1199.99

3. Product: PowerLite Convertible
   Category: Computers and Laptops
   Brand: PowerLite
   Model Number: PL-CV300
   Warranty: 1 year
   Rating: 4.3
   Features: 14-inch touchscreen, 8GB RAM, 256GB SSD, 360-degree hinge
   Description: A versatile convertible laptop with a responsive touchscreen.
   Price: $699.99

4. Product: TechPro Desktop
   Category: Computers and Laptops
   Brand: TechPro
   Model Number: TP-DT500
   Warranty: 1 year
   Rating: 4.4
   Features: Intel Core i7 processor, 16GB RAM, 1TB HDD, NVIDIA GeForce GTX 1660
   Description: A powerful desktop computer for work and play.
   Price: $999.99

5. Product: BlueWave Chromebook
   Category: Computers and Laptops
   Brand: BlueWave
   Model Number: BW-CB100
   Warranty: 1 year
   Rating: 4.1
   Features: 11.6-inch display, 4GB RAM, 32GB eMMC, Chrome OS
   Description: A compact and affordable Chromebook for everyday tasks.
   Price: $249.99

Step 3: {delimiter} If the message contains products \
in the list above, list any assumptions that the \
user is making in their \
message e.g. that Laptop X is bigger than \
Laptop Y, or that Laptop Z has a 2 year warranty.

Step 4: {delimiter}: If the user made any assumptions, \
figure out whether the assumption is true based on your \
product information. 

Step 5: {delimiter}: First, politely correct the \
customer's incorrect assumptions if applicable. \
Only mention or reference products in the list of \
5 available products, as these are the only 5 \
products that the store sells. \
Answer the customer in a friendly tone.

Use the following format:
Step 1: {delimiter} <step 1 reasoning>
Step 2: {delimiter} <step 2 reasoning>
Step 3: {delimiter} <step 3 reasoning>
Step 4: {delimiter} <step 4 reasoning>
Response to user: {delimiter} <response to customer>

Make sure to include {delimiter} to separate every step. 

"""

User message

user_message = f"""
by how much is the BlueWave Chromebook more expensive \
than the TechPro Desktop"""
messages = [
    {'role': 'system',
     'content': system_message},
    {'role': 'user',
     'content': f"{delimiter}{user_message}{delimiter}"},
]

Response

response = get_completion_from_messages(messages)
print(response)

Step 1: ## The user is asking about the price difference between the BlueWave Chromebook and the TechPro Desktop.

Step 2: ## Both the BlueWave Chromebook and the TechPro Desktop are available products.

Step 3: ## The user assumes that the BlueWave Chromebook is more expensive than the TechPro Desktop.

Step 4: ## The assumption is incorrect. The TechPro Desktop is priced at $999.99, while the BlueWave Chromebook is priced at $249.99. Therefore, the BlueWave Chromebook is actually less expensive than the TechPro Desktop.

Response to user: ## The BlueWave Chromebook is actually less expensive than the TechPro Desktop. The BlueWave Chromebook is priced at $249.99, while the TechPro Desktop is priced at $999.99. Let me know if there’s anything else I can assist you with

Chain-of-Thought Prompting 2

User message

user_message = f"""do you sell tvs"""
messages = [
    {'role': 'system',
     'content': system_message},
    {'role': 'user',
     'content': f"{delimiter}{user_message}{delimiter}"},
]

Response

response = get_completion_from_messages(messages)
print(response)
  • Step 1:## The user is asking if the store sells TVs. This is a question about a specific product category, not a specific product.

Inner Monologue

  • Since we asked the LLM to separate its reasoning steps by a delimiter, we can hide the chain-of-thought reasoning from the final output that the user sees.
try:
    final_response = response.split(delimiter)[-1].strip()
except Exception as e:
    final_response = "Sorry, I'm having trouble right now, please try asking another question."

print(final_response)
  • The BlueWave Chromebook is actually less expensive than the TechPro Desktop. The BlueWave Chromebook is priced at $249.99, while the TechPro Desktop is priced at $999.99. Let me know if there’s anything else I can assist you with!

Acknowledgments

This tutorial is mainly based on the excellent course “Building Systems with the ChatGPT API” provided by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI

What’s next?

Congratulations! You have completed this tutorial 👍

Next, you may want to go back to the lab’s website

Jan Kirenz