A/B testing prompt variations in LangChain

Introduction

A/B testing prompt variations helps you find which prompt works best by comparing different versions.

You want to see which prompt gets better answers from the AI.

You are unsure how to phrase a question for best results.

You want to improve user experience by testing different prompt styles.

You want to compare performance of multiple prompt ideas quickly.

Syntax

LangChain

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

prompt_a = PromptTemplate(input_variables=["input"], template="Tell me a joke about {input}.")
prompt_b = PromptTemplate(input_variables=["input"], template="Make a funny story about {input}.")

chain_a = LLMChain(llm=llm, prompt=prompt_a)
chain_b = LLMChain(llm=llm, prompt=prompt_b)

response_a = chain_a.run(input="cats")
response_b = chain_b.run(input="cats")

Use different PromptTemplate objects for each variation.

Run each prompt through the LLMChain separately to compare outputs.

Examples

Two prompt variations for poetry and story writing.

LangChain

prompt1 = PromptTemplate(input_variables=["topic"], template="Write a poem about {topic}.")
prompt2 = PromptTemplate(input_variables=["topic"], template="Write a short story about {topic}.")

Run both prompts with the same input to compare results.

LangChain

chain1 = LLMChain(llm=llm, prompt=prompt1)
chain2 = LLMChain(llm=llm, prompt=prompt2)

result1 = chain1.run(topic="rain")
result2 = chain2.run(topic="rain")

Sample Program

This example creates two prompt versions about dogs. It runs both and prints their outputs to compare which is funnier or better.

LangChain

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

llm = OpenAI(temperature=0.7)

prompt_a = PromptTemplate(input_variables=["input"], template="Tell me a joke about {input}.")
prompt_b = PromptTemplate(input_variables=["input"], template="Make a funny story about {input}.")

chain_a = LLMChain(llm=llm, prompt=prompt_a)
chain_b = LLMChain(llm=llm, prompt=prompt_b)

response_a = chain_a.run(input="dogs")
response_b = chain_b.run(input="dogs")

print("Prompt A response:", response_a)
print("Prompt B response:", response_b)

Important Notes

Keep prompt variations simple and focused on one change at a time.

Use the same input for fair comparison.

Check outputs carefully to decide which prompt works best.

Summary

A/B testing helps find the best prompt by comparing different versions.

Create separate PromptTemplate objects for each variation.

Run each prompt with the same input and compare results.

Practice

1. What is the main purpose of using A/B testing with prompt variations in Langchain?

easy

A. To compare different prompt versions and find the best one

B. To speed up the execution of a single prompt

C. To combine multiple prompts into one

D. To automatically fix errors in prompts

Solution

1. Step 1: Understand A/B testing concept

   A/B testing means comparing two or more versions to see which works better.

2. Step 2: Apply to prompt variations

   In Langchain, this means running different prompt templates and comparing their outputs.

3. Final Answer:

   To compare different prompt versions and find the best one -> Option A

4. Quick Check:

   A/B testing = Compare versions [OK]

Hint: A/B testing means comparing versions to pick the best [OK]

Common Mistakes:

• Thinking A/B testing speeds up prompts
• Believing it merges prompts automatically
• Assuming it fixes prompt errors

2. Which of the following is the correct way to create two prompt variations for A/B testing in Langchain using the 'template=' keyword argument for both PromptTemplates?

easy

A. prompt1 = PromptTemplate('Hello {name}'); prompt2 = PromptTemplate(template='Hi {name}')

B. prompt1 = PromptTemplate('Hello {name}'); prompt2 = PromptTemplate('Hi {name}')

C. prompt1 = PromptTemplate(template='Hello {name}'); prompt2 = PromptTemplate(template='Hi {name}')

D. prompt1 = PromptTemplate(template='Hello {name}'); prompt2 = PromptTemplate('Hi {name}')

Solution

1. Step 1: Check PromptTemplate syntax

   PromptTemplate uses the named argument 'template' to define the prompt string.

2. Step 2: Verify both prompts use correct syntax

   Only prompt1 = PromptTemplate(template='Hello {name}'); prompt2 = PromptTemplate(template='Hi {name}') uses PromptTemplate(template='...') for both prompts correctly.

3. Final Answer:

   prompt1 = PromptTemplate(template='Hello {name}'); prompt2 = PromptTemplate(template='Hi {name}') -> Option C

4. Quick Check:

   Use template= keyword for PromptTemplate [OK]

Hint: PromptTemplate needs template='...' argument [OK]

Common Mistakes:

• Omitting the 'template=' keyword
• Mixing positional and keyword arguments
• Using incorrect string syntax

3. Given the code below, what will be the output of print(results)?

from langchain import PromptTemplate
prompt1 = PromptTemplate(template='Hello {name}')
prompt2 = PromptTemplate(template='Hi {name}')
inputs = {'name': 'Alice'}
results = [prompt1.format(**inputs), prompt2.format(**inputs)]
print(results)

medium

A. ['Hello Alice', 'Hi Alice']

B. ['Hello {name}', 'Hi {name}']

C. ['Hello', 'Hi']

D. Error: format method not found

Solution

1. Step 1: Understand PromptTemplate.format()

   The format method replaces placeholders like {name} with values from inputs.

2. Step 2: Apply inputs to both prompts

   Both prompts get 'Alice' for {name}, so outputs are 'Hello Alice' and 'Hi Alice'.

3. Final Answer:

   ['Hello Alice', 'Hi Alice'] -> Option A

4. Quick Check:

   format() replaces placeholders correctly [OK]

Hint: format() fills placeholders with input values [OK]

Common Mistakes:

• Thinking format() returns template string unchanged
• Expecting placeholders to remain in output
• Assuming format() method does not exist

4. Identify the error in this A/B testing code snippet:

from langchain import PromptTemplate
prompt1 = PromptTemplate(template='Hello {name}')
prompt2 = PromptTemplate(template='Hi {name}')
inputs = {'name': 'Bob'}
results = [prompt1.format(inputs), prompt2.format(inputs)]
print(results)

medium

A. PromptTemplate missing template argument

B. Using format() without unpacking inputs dictionary

C. inputs dictionary missing required key

D. print statement syntax error

Solution

1. Step 1: Check how format() is called

   format() expects keyword arguments, so inputs must be unpacked with **inputs.

2. Step 2: Identify the error

   Code passes inputs as a single dict argument, causing a TypeError.

3. Final Answer:

   Using format() without unpacking inputs dictionary -> Option B

4. Quick Check:

   Use **inputs to unpack dict for format() [OK]

Hint: Always unpack dict with ** when calling format() [OK]

Common Mistakes:

• Passing dict directly instead of unpacking
• Forgetting to import PromptTemplate
• Using wrong print syntax

5. You want to run A/B testing on three prompt variations and select the best output based on a scoring function. Which approach correctly implements this in Langchain?

hard

A. Use a loop to create prompts but do not run format(), just score the templates

B. Create one PromptTemplate with all variations combined, run format() once, then score the single output

C. Run format() on one prompt, then copy the output three times and score them

D. Create three PromptTemplate objects, run format() on each with inputs, then apply the scoring function to outputs and pick the highest score

Solution

1. Step 1: Understand A/B testing with multiple prompts

   You need separate prompt templates for each variation to test them individually.

2. Step 2: Run each prompt with the same inputs and score outputs

   Format each prompt with inputs, then apply scoring to compare results.

3. Step 3: Select the best output based on scores

   Pick the output with the highest score as the best prompt result.

4. Final Answer:

   Create three PromptTemplate objects, run format() on each with inputs, then apply the scoring function to outputs and pick the highest score -> Option D

5. Quick Check:

   Separate prompts + score outputs = best choice [OK]

Hint: Run all prompts, score outputs, pick highest score [OK]

Common Mistakes:

• Combining prompts into one string
• Scoring templates instead of outputs
• Not running format() before scoring