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

Concept Flow - A/B testing prompt variations

Define Prompt A

↓

Define Prompt B

↓

Send Input to Both Prompts

↓

Receive Responses A and B

↓

Compare Responses

↓

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.

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