Few-shot prompt templates in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a few-shot prompt template in Langchain?

A few-shot prompt template is a way to give an AI model a few examples of input-output pairs within the prompt. This helps the model understand the task better by showing it how to respond before asking it to generate new answers.

beginner

How does few-shot prompting improve AI responses?

Few-shot prompting improves AI responses by providing examples that guide the model on what kind of answers are expected. It’s like showing a friend how to do a task before asking them to do it themselves.

intermediate

In Langchain, what are the main parts of a few-shot prompt template?

The main parts are: 1) The prefix or instruction that explains the task, 2) The examples section with input-output pairs, and 3) The suffix where the new input is placed for the model to complete.

beginner

Why is it important to keep examples clear and relevant in few-shot prompts?

Clear and relevant examples help the AI model understand exactly what is expected. Confusing or unrelated examples can make the model give wrong or unclear answers.

intermediate

Show a simple example of a few-shot prompt template in Langchain.

Example: A prompt with a prefix 'Translate English to French:', examples like 'Hello -> Bonjour', 'Goodbye -> Au revoir', and then a suffix with the new English word to translate. The model uses these examples to translate the new word.

What does a few-shot prompt template provide to the AI model?

A Examples of input-output pairs

B Only instructions without examples

C Random text unrelated to the task

D A list of possible answers

Few-shot prompt templates give examples to guide the AI on how to respond.

Which part of a few-shot prompt template contains the new input for the model to answer?

A Prefix

B Examples section

C Suffix

D Metadata

The suffix is where the new input is placed for the model to complete.

Why should examples in few-shot prompts be clear and relevant?

A To help the model understand the task

B To confuse the model

C To make the prompt longer

D To reduce the model's accuracy

Clear examples help the model understand what kind of answers to give.

Few-shot prompting is similar to:

A Giving no instructions

B Asking a question without context

C Ignoring examples

D Showing a friend how to do a task before they try

Few-shot prompting shows examples first, like teaching a friend before they try.

In Langchain, which of these is NOT part of a few-shot prompt template?

A Prefix with instructions

B Database connection string

C Suffix with new input

D Examples section

Database connection strings are unrelated to prompt templates.

Explain what a few-shot prompt template is and why it helps AI models.

Think about how showing examples helps someone learn a new task.

Describe the structure of a few-shot prompt template in Langchain and the purpose of each part.

Consider the order: instructions, examples, then the question.

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