Few-shot prompt templates in LangChain - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output of this few-shot prompt template code?

Consider this LangChain few-shot prompt template code snippet. What will be the final prompt text after rendering?

LangChain

from langchain.prompts import FewShotPromptTemplate, PromptTemplate

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

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

few_shot = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    prefix="You are a helpful assistant.",
    suffix="Input: {user_input}\nResponse:",
    input_variables=["user_input"]
)

final_prompt = few_shot.format(user_input="Hello")
print(final_prompt)

A

You are a helpful assistant.
Input: Hello
Response: Hi there!
Input: Bye
Response: Goodbye!
Input: Hello
Response:

B

You are a helpful assistant.
Input: Hello
Response: Hi there!
Input: Bye
Response: Goodbye!
Input: Hello
Response: Hi there!

C

You are a helpful assistant.
Input: Hello
Response: Hi there!
Input: Bye
Response: Goodbye!
Response: Hello
Input:

D

You are a helpful assistant.
Input: Hello
Response: Goodbye!
Input: Bye
Response: Hi there!
Input: Hello
Response:

💡 Hint

Look at how the examples are formatted and how the suffix uses the user input.

The FewShotPromptTemplate inserts all examples formatted by example_prompt between prefix and suffix. The suffix includes the new user input, waiting for the model's response.

📝 Syntax

intermediate

Which option correctly defines a FewShotPromptTemplate with multiple input variables?

You want to create a FewShotPromptTemplate that uses examples with inputs 'question' and 'answer', and a suffix that takes 'new_question'. Which code snippet is correct?

A

FewShotPromptTemplate(
  examples=examples,
  example_prompt=PromptTemplate(input_variables=["question", "answer"], template="Q: {question}\nA: {answer}"),
  prefix="Answer the questions.",
  suffix="Q: {new_question}\nA:",
  input_variables=["answer"]
)

B

FewShotPromptTemplate(
  examples=examples,
  example_prompt=PromptTemplate(input_variables=["question", "answer"], template="Q: {question}\nA: {answer}"),
  prefix="Answer the questions.",
  suffix="Q: {question}\nA:",
  input_variables=["new_question"]
)

C

FewShotPromptTemplate(
  examples=examples,
  example_prompt=PromptTemplate(input_variables=["question", "answer"], template="Q: {question}\nA: {answer}"),
  prefix="Answer the questions.",
  suffix="Q: {new_question}\nA:",
  input_variables=["question"]
)

D

FewShotPromptTemplate(
  examples=examples,
  example_prompt=PromptTemplate(input_variables=["question", "answer"], template="Q: {question}\nA: {answer}"),
  prefix="Answer the questions.",
  suffix="Q: {new_question}\nA:",
  input_variables=["new_question"]
)

💡 Hint

Check that the suffix uses the same variable name as in input_variables.

The suffix uses {new_question}, so input_variables must include 'new_question'. The example_prompt uses 'question' and 'answer' for examples.

🔧 Debug

advanced

What error does this FewShotPromptTemplate code raise?

This code tries to create a FewShotPromptTemplate but fails. What error will it raise?

LangChain

from langchain.prompts import FewShotPromptTemplate, PromptTemplate

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

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

few_shot = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    prefix="Start conversation.",
    suffix="Input: {user_input}\nOutput:",
    input_variables=["user_input", "extra"]
)

few_shot.format(user_input="Hi")

A No error, outputs the formatted prompt

B KeyError: 'extra'

C ValueError: input_variables contains variables not in suffix or prefix

D TypeError: format() missing 1 required positional argument

💡 Hint

Check which variables are used in suffix and prefix versus input_variables.

The input_variables list includes 'extra' which is not used in prefix or suffix, so when formatting without 'extra', a KeyError occurs.

❓ state_output

advanced

What is the number of example blocks in the rendered prompt?

Given this FewShotPromptTemplate with 3 examples, how many example blocks appear in the final prompt string?

LangChain

examples = [
  {"q": "What is AI?", "a": "Artificial Intelligence."},
  {"q": "What is ML?", "a": "Machine Learning."},
  {"q": "What is NLP?", "a": "Natural Language Processing."}
]

example_prompt = PromptTemplate(
  input_variables=["q", "a"],
  template="Q: {q}\nA: {a}"
)

few_shot = FewShotPromptTemplate(
  examples=examples,
  example_prompt=example_prompt,
  prefix="Answer the following questions.",
  suffix="Q: {new_q}\nA:",
  input_variables=["new_q"]
)

prompt_text = few_shot.format(new_q="What is Python?")

A 4

B 1

C 3

D 0

💡 Hint

Count how many example dictionaries are in the examples list.

The examples list has 3 items, so 3 example blocks appear in the prompt.

🧠 Conceptual

expert

Which statement best describes the role of 'example_prompt' in FewShotPromptTemplate?

In LangChain's FewShotPromptTemplate, what is the main purpose of the 'example_prompt' parameter?

A It defines how each example dictionary is formatted into text before being inserted into the prompt.

B It specifies the variables required by the suffix template for new inputs.

C It controls the order in which examples appear in the final prompt.

D It validates the types of inputs passed to the FewShotPromptTemplate.

💡 Hint

Think about how examples become text in the prompt.

The example_prompt is a PromptTemplate that formats each example dictionary into a text block to be included in the prompt.

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