A/B testing prompt variations in LangChain - Step-by-Step Execution

Concept Flow - A/B testing prompt variations

Define Prompt A

↓

Define Prompt B

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Send Input to Both Prompts

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Receive Responses A and B

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Compare Responses

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Choose Best Prompt or Analyze Results

This flow shows how two prompt versions are tested by sending the same input, collecting responses, and comparing them to find the better prompt.

Execution Sample

LangChain

from langchain.prompts import PromptTemplate

prompt_a = PromptTemplate(template="Hello {name}, how are you?")
prompt_b = PromptTemplate(template="Hi {name}! What's up?")

input_vars = {"name": "Alice"}
response_a = prompt_a.format(**input_vars)
response_b = prompt_b.format(**input_vars)

This code creates two prompt templates and formats them with the same input to get two different prompt outputs.

Execution Table

Step	Action	Prompt Template	Input	Output
1	Define Prompt A	Hello {name}, how are you?	-	-
2	Define Prompt B	Hi {name}! What's up?	-	-
3	Format Prompt A	Hello {name}, how are you?	{"name": "Alice"}	Hello Alice, how are you?
4	Format Prompt B	Hi {name}! What's up?	{"name": "Alice"}	Hi Alice! What's up?
5	Compare Outputs	-	-	Decide which prompt sounds better or test with model

💡 Both prompts formatted with input; ready for model testing or comparison.

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
prompt_a	PromptTemplate object	No change	No change	No change
prompt_b	PromptTemplate object	No change	No change	No change
input_vars	{"name": "Alice"}	No change	No change	No change
response_a	None	Hello Alice, how are you?	No change	Hello Alice, how are you?
response_b	None	No change	Hi Alice! What's up?	Hi Alice! What's up?

Key Moments - 2 Insights

Why do we format both prompts with the same input?

Formatting both prompts with the same input ensures a fair comparison of how each prompt handles identical data, as shown in steps 3 and 4 of the execution_table.

What does 'Compare Outputs' mean in this context?

It means reviewing the formatted prompt strings or sending them to a model to see which prompt generates better responses, as indicated in step 5 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table at step 3. What is the output of formatting Prompt A?

A Hello {name}, how are you?

B Hello Alice, how are you?

C Hi Alice! What's up?

D Hi {name}! What's up?

💡 Hint

Check the 'Output' column in step 3 of the execution_table.

At which step do we see the formatted output for Prompt B?

A Step 4

B Step 2

C Step 5

D Step 3

💡 Hint

Look at the 'Output' column for Prompt B formatting in the execution_table.

If the input variable 'name' changed to 'Bob', how would the output at step 3 change?

A Hello Alice, how are you?

B Hi Alice! What's up?

C Hello Bob, how are you?

D Hi Bob! What's up?

💡 Hint

Refer to variable_tracker and how input_vars affect formatted outputs.

Concept Snapshot

A/B testing prompt variations:
- Define multiple prompt templates.
- Format each with the same input data.
- Compare outputs to find the best prompt.
- Use consistent input for fair testing.
- Helps improve prompt effectiveness.

Full Transcript

This visual execution shows how to do A/B testing with prompt variations in Langchain. First, we define two prompt templates with placeholders. Then, we format both prompts using the same input variable, for example, the name 'Alice'. The formatted outputs are 'Hello Alice, how are you?' and 'Hi Alice! What's up?'. Next, we compare these outputs to decide which prompt sounds better or to test them with a language model. Tracking variables shows how prompt templates and responses change step by step. This method helps improve prompt design by testing different versions fairly.