Few-shot prompt templates in LangChain

Introduction

Few-shot prompt templates help you teach AI models by showing a few examples before asking a question. This makes the AI understand better what you want.

When you want the AI to follow a pattern shown in examples.

When you want to improve AI answers by giving it context.

When you want to create a prompt that includes example questions and answers.

When you want to reuse a prompt with different inputs but same examples.

When you want to guide AI to perform tasks like translation or classification.

Syntax

LangChain

from langchain.prompts import FewShotPromptTemplate, PromptTemplate

example_prompt = PromptTemplate(
    input_variables=["input", "output"],
    template="Input: {input}\nOutput: {output}"
)

examples = [
    {"input": "Hello", "output": "Hi!"},
    {"input": "Bye", "output": "Goodbye!"}
]

few_shot_prompt = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    prefix="Answer the following greetings:",
    suffix="Input: {input}",
    input_variables=["input"]
)

The examples list holds example input-output pairs.

The prefix and suffix add text before and after examples.

Examples

This example shows how to create a few-shot prompt with different greetings.

LangChain

examples = [
    {"input": "Good morning", "output": "Morning!"},
    {"input": "Good night", "output": "Sleep well!"}
]

few_shot_prompt = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    prefix="Respond to greetings:",
    suffix="Input: {input}",
    input_variables=["input"]
)

This formats the prompt by inserting the input after the examples and prefix.

LangChain

few_shot_prompt.format(input="Hello there")

Sample Program

This program creates a few-shot prompt with two example words and definitions. Then it formats the prompt to ask for the definition of "JavaScript".

LangChain

from langchain.prompts import FewShotPromptTemplate, PromptTemplate

example_prompt = PromptTemplate(
    input_variables=["word", "definition"],
    template="Word: {word}\nDefinition: {definition}"
)

examples = [
    {"word": "Python", "definition": "A programming language."},
    {"word": "Java", "definition": "Another programming language."}
]

few_shot_prompt = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    prefix="Here are some words and their definitions:",
    suffix="Word: {word}\nDefinition:",
    input_variables=["word"]
)

prompt_text = few_shot_prompt.format(word="JavaScript")
print(prompt_text)

Important Notes

Keep examples clear and relevant to help the AI understand the task.

You can change the prefix and suffix to guide the AI's response style.

Use format to insert new inputs into the prompt before sending to the AI.

Summary

Few-shot prompt templates show examples to teach AI how to respond.

They combine example prompts, example data, and extra text around them.

This helps AI give better, more accurate answers.

Practice

1. What is the main purpose of a few-shot prompt template in Langchain?

easy

A. To provide example prompts and responses to guide AI behavior

B. To store large datasets for training AI models

C. To execute code on the AI server

D. To create user interfaces for AI applications

Solution

1. Step 1: Understand few-shot prompt templates

   Few-shot prompt templates include example prompts and responses to teach AI how to answer.

2. Step 2: Identify the main purpose

   The main goal is to guide AI behavior by showing examples, not to store data or create interfaces.

3. Final Answer:

   To provide example prompts and responses to guide AI behavior -> Option A

4. Quick Check:

   Few-shot prompt templates guide AI with examples = A [OK]

Hint: Remember: few-shot means showing examples to teach AI [OK]

Common Mistakes:

• Confusing prompt templates with data storage
• Thinking they run code instead of guiding AI
• Assuming they build UI components

2. Which of the following is the correct way to create a few-shot prompt template in Langchain?

easy

A. FewShotPromptTemplate(data=examples, prompt=example_prompt, suffix=prefix_text)

B. FewShotPromptTemplate(samples=examples, prompt_template=example_prompt, header=prefix_text)

C. FewShotPromptTemplate(examples=examples, example_prompt=example_prompt, prefix=prefix_text)

D. FewShotPromptTemplate(inputs=examples, prompt=example_prompt, footer=prefix_text)

Solution

1. Step 1: Recall Langchain few-shot prompt syntax

   The correct constructor uses parameters: examples, example_prompt, and prefix.

2. Step 2: Match parameters to options

   Only FewShotPromptTemplate(examples=examples, example_prompt=example_prompt, prefix=prefix_text) uses the exact parameter names required by Langchain's FewShotPromptTemplate.

3. Final Answer:

   FewShotPromptTemplate(examples=examples, example_prompt=example_prompt, prefix=prefix_text) -> Option C

4. Quick Check:

   Correct parameter names = B [OK]

Hint: Check parameter names exactly as in Langchain docs [OK]

Common Mistakes:

• Using wrong parameter names like data or samples
• Mixing prefix with suffix or footer
• Confusing example_prompt with prompt_template

3. Given this code snippet, what will be the output of print(prompt_template.format(input="Translate to French: Hello") )?

examples = [{"input": "Hello", "output": "Bonjour"}]
example_prompt = PromptTemplate(input_variables=["input", "output"], template="Input: {input}\nOutput: {output}")
prompt_template = FewShotPromptTemplate(examples=examples, example_prompt=example_prompt, prefix="Translate English to French:\n", suffix="\nInput: {input}\nOutput:", input_variables=["input"])

medium

A. Translate English to French:
Input: Translate to French: Hello
Output:

B. Translate English to French:
Input: Hello
Output: Bonjour
Translate to French: Hello

C. Input: Hello
Output: Bonjour
Translate English to French: Hello

D. Translate English to French:
Input: Hello
Output: Bonjour
Input: Translate to French: Hello
Output:

Solution

1. Step 1: Understand few-shot prompt formatting

   The prompt includes the prefix, then example prompts formatted with example data, then the new input prompt.

2. Step 2: Apply formatting to given input

   The prefix is "Translate English to French:", then example "Input: Hello\nOutput: Bonjour", then the new input "Input: Translate to French: Hello\nOutput:" (empty output to be filled by AI).

3. Final Answer:

   Translate English to French: Input: Hello Output: Bonjour Input: Translate to French: Hello Output: -> Option D

4. Quick Check:

   Prefix + example + new input prompt = C [OK]

Hint: Few-shot templates show prefix, examples, then new input [OK]

Common Mistakes:

• Ignoring prefix text in output
• Not formatting new input as 'Input: ... Output:'
• Confusing example data with new input

4. What is the error in this code snippet that tries to create a few-shot prompt template?

examples = [{"input": "Hi", "output": "Salut"}]
example_prompt = PromptTemplate(input_variables=["input", "output"], template="Input: {input}\nOutput: {output}")
prompt_template = FewShotPromptTemplate(examples=examples, example_prompt=example_prompt, prefix="Translate:")
print(prompt_template.format(input="Hello"))

medium

A. PromptTemplate cannot have input_variables

B. Missing input_variables parameter in FewShotPromptTemplate constructor

C. Prefix must be a function, not a string

D. examples list should be empty for FewShotPromptTemplate

Solution

1. Step 1: Check FewShotPromptTemplate required parameters

   FewShotPromptTemplate requires input_variables parameter to know which inputs to expect.

2. Step 2: Identify missing parameter

   The code misses input_variables in FewShotPromptTemplate, causing an error when calling format.

3. Final Answer:

   Missing input_variables parameter in FewShotPromptTemplate constructor -> Option B

4. Quick Check:

   input_variables missing = D [OK]

Hint: Always include input_variables when creating prompt templates [OK]

Common Mistakes:

• Omitting input_variables in FewShotPromptTemplate
• Thinking prefix must be a function
• Assuming examples can be empty

5. You want to create a few-shot prompt template that filters out examples with empty outputs before formatting. Which approach correctly applies this filtering in Langchain?

hard

A. Filter the examples list before passing it to FewShotPromptTemplate constructor

B. Use a custom example_prompt that skips empty outputs during formatting

C. Set prefix to None and rely on Langchain to ignore empty outputs

D. Pass all examples and filter outputs after calling prompt_template.format()

Solution

1. Step 1: Understand filtering in few-shot templates

   FewShotPromptTemplate uses the examples list as-is; filtering must happen before passing examples.

2. Step 2: Evaluate options for filtering

   Only filtering the examples list before creating the template ensures empty outputs are excluded properly.

3. Final Answer:

   Filter the examples list before passing it to FewShotPromptTemplate constructor -> Option A

4. Quick Check:

   Pre-filter examples before constructor = A [OK]

Hint: Filter examples before creating the prompt template [OK]

Common Mistakes:

• Trying to filter inside example_prompt formatting
• Assuming Langchain auto-filters empty outputs
• Filtering after formatting instead of before