A/B testing prompt variations in LangChain - Mini Project: Build & Apply

A/B Testing Prompt Variations with LangChain

📖 Scenario: You are building a chatbot that tries different prompt styles to see which one gets better answers from a language model.This helps you find the best way to ask questions.

🎯 Goal: Create a LangChain setup that holds two prompt variations, sends them to the language model, and compares the responses.

📋 What You'll Learn

Create a dictionary called prompt_variations with two exact prompt strings.

Create a variable called model_name set to 'gpt-4o-mini'.

Use a for loop with variables variation_name and prompt_text to iterate over prompt_variations.items().

Create a LangChain PromptTemplate and LLMChain for each prompt variation and store the responses in a dictionary called responses.

💡 Why This Matters

🌍 Real World

A/B testing prompt variations helps chatbot developers find the best way to ask questions to get clearer or more useful answers from AI models.

💼 Career

This skill is useful for AI developers, prompt engineers, and anyone working with language models to optimize user interactions.

1

Create the prompt variations dictionary

Create a dictionary called prompt_variations with these exact entries: 'variation_a': 'Explain the benefits of exercise in simple terms.' and 'variation_b': 'List three reasons why exercise is good for health.'

LangChain

# Create the prompt_variations dictionary with two exact prompts
# Your code here

Need a hint?

Use curly braces to create a dictionary with two keys: 'variation_a' and 'variation_b'.

2

Set the model name variable

Create a variable called model_name and set it to the string 'gpt-4o-mini'.

LangChain

prompt_variations = {
    'variation_a': 'Explain the benefits of exercise in simple terms.',
    'variation_b': 'List three reasons why exercise is good for health.'
}
# Create the model_name variable and set it to 'gpt-4o-mini'
# Your code here

Need a hint?

Just assign the string 'gpt-4o-mini' to the variable model_name.

3

Create the LangChain prompt templates and chains

Use a for loop with variables variation_name and prompt_text to iterate over prompt_variations.items(). Inside the loop, create a PromptTemplate with input_variables=[] and template=prompt_text. Then create an LLMChain with llm=ChatOpenAI(model_name=model_name) and the prompt template. Store the chain in a dictionary called chains with keys as variation_name.

LangChain

prompt_variations = {
    'variation_a': 'Explain the benefits of exercise in simple terms.',
    'variation_b': 'List three reasons why exercise is good for health.'
}

model_name = 'gpt-4o-mini'

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Create a dictionary called chains
# Use a for loop to create PromptTemplate and LLMChain for each prompt
# Your code here

Need a hint?

Use a for loop to create prompt templates and chains, then save them in the chains dictionary.

4

Run the chains and collect responses

Create an empty dictionary called responses. Use a for loop with variables variation_name and chain to iterate over chains.items(). Inside the loop, call chain.run() and store the result in responses[variation_name].

LangChain

prompt_variations = {
    'variation_a': 'Explain the benefits of exercise in simple terms.',
    'variation_b': 'List three reasons why exercise is good for health.'
}

model_name = 'gpt-4o-mini'

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

chains = {}
for variation_name, prompt_text in prompt_variations.items():
    prompt = PromptTemplate(input_variables=[], template=prompt_text)
    chain = LLMChain(llm=ChatOpenAI(model_name=model_name), prompt=prompt)
    chains[variation_name] = chain

# Create a dictionary called responses
# Use a for loop to run each chain and save the output
# Your code here

Need a hint?

Run each chain and save the answers in the responses dictionary using a for loop.