Few-Shot Prompting: Techniques, Examples, and Best Practices

Introduction

Few-shot prompting is becoming a hot topic in natural language processing. Few-shot prompting techniques allow developers, researchers, and businesses to get high-quality, context-specific answers without fine-tuning. However, what exactly is few-shot prompting, and why is it so important?
This article seeks to unpack those questions and more. Here’s what we’ll dive into:

• The fundamentals of few-shot prompting and few-shot learning.
• How in-context learning and prompt engineering fit into the picture.
• The differences between zero-shot prompting, one-shot prompting, and few-shot prompting.
• Examples of few-shot prompting featuring Python code snippets using the OpenAI API and frameworks like LangChain.
• Best practices include managing tokens, mixing various task types, and creating high-quality prompts.
• Real-life applications like classification, summarization, and code generation.
• Common pitfalls and tips to avoid them.

Prerequisites

• Basic understanding of large language models and how they process input and generate responses.
• A solid understanding of Python programming, especially when working with APIs and libraries like OpenAI or LangChain.
• An active OpenAI account with a valid API key and access to chat models like gpt-3.5-turbo, gpt-4, etc.
• Installed dependencies such as OpenAI and LangChain via pip.
• Ability to create clear input-output examples for tasks like text classification, summarization, and code conversion.

Understanding Few-Shot Prompting

Few-shot prompting is a technique that involves supplying a language model with a handful of input-output examples (or “shots”) within the prompt. Instead of updating the model’s internal settings as you would in fine-tuning, you provide a thoughtfully designed prompt that displays a few demonstrations of the task.

By doing this, the model can recognize the pattern or context of these examples. Essentially, the model uses the given examples to infer how to tackle new, unseen queries that follow a similar structure or logic.

How Does In-Context Learning Work?

The mechanism of in-context learning enables few-shot prompting to work effectively. It operates through the following process:

• Sequence Modeling: The model interprets the complete prompt, consisting of examples and the new input queries or tasks, as a continuous sequence of tokens.
• Pattern Extraction: The model analyzes patterns from the example inputs and outputs to guide its response to the new query.
• Prediction: By leveraging its pre-trained language understanding with patterns extracted from limited examples, the model predicts the most likely next tokens for the new task.

The model does not need gradient updates or parameter changes because all operations occur through the prompt’s instructions.

Few-Shot Learning vs Zero-Shot Prompting vs One-Shot Prompting

When discussing few-shot prompting, it’s helpful to understand the differences between few-shot, zero-shot, and one-shot prompting. The table below summarizes the various prompting techniques used in language models:

Each prompting strategy offers a balance between task complexity and the amount of context the model needs to perform effectively.

Few-Shot Prompting Examples

Here are some practical use cases where few-shot prompting can be applied, along with examples of how a prompt might look.

Text Classification (Sentiment Analysis example)

You can steer a language model to classify text sentiment, topic, spam, non-spam, etc., by providing a few labeled examples. Let’s consider the following example where we want to determine if a movie review is positive or negative:

Determine if each movie review is Positive or Negative.
Review: "I couldn't stop laughing throughout the film!"
Sentiment: Positive
Review: "The plot was a complete mess and very boring."
Sentiment: Negative
Review: "The cinematography was stunning and the story touched my heart."
Sentiment:

In this case, we provide two review examples with their sentiments. These few-shot examples help the model learn the format (Review: … Sentiment: …) and distinguish between positive and negative language. By recognizing the pattern, the model generalizes and determines that the third review is Positive, as the words “stunning” and “touched my heart” indicate positive feelings.

Text Summarization

The summarization style can be controlled via a few-shot examples. In this context, we present two article-summary pairs. Next, we introduce a third article and ask the model to generate a one-sentence summary.

Article: Astronomers identified an exoplanet that might contain water.
Summary: The recent exoplanet discovery inspires optimism about its potential to hold water and sustain life.
Article: Tech company earnings reports triggered a substantial rally in the stock market.
Summary: Investors responded favorably to tech earnings reports resulting in a robust stock market rally.
Article: Community backing helped the city council approve the development plan for the new park.
Summary:

Code Generation

This prompt provides two examples of pseudocode and their Python translation. The model is then tasked with generating Python code for a third pseudocode snippet. These examples guide the model with important syntax details, like using the Python print function and structuring loops.

Pseudocode:

x = 20 if x > 0: print "Positive" else: print "Non-positive"

Python:

x = 20
if x > 0:
    print("Positive")
else:
    print("Non-positive")

Pseudocode:

for each number i in range 1 to 20:
    print i

Python:

for i in range(1, 20):
    print(i)

Pseudocode:

set total to 0
for each item price in list:
    add price to total
print total

Practical Usage in GPT-4, ChatGPT, Claude, and More

Few-shot prompting works similarly across many modern LLM platforms:

• GPT-4 / ChatGPT: The model can be guided using system messages or user prompts that include a few examples. The GPT-4 model features complex language understanding capabilities designed to perform exceptionally well with in-context learning tasks.
• Claude (Anthropic): Claude uses OpenAI’s similar technique by providing a conversation or context with few-shot examples to direct its response style and format.

Implementing Few-Shot Prompting with OpenAI API and LangChain

With an understanding of few-shot prompting and several illustrative examples, let us transition to practical implementation techniques. We will explore two approaches.

Few-Shot Prompting via OpenAI API

The script below uses the OpenAI Python SDK to convert Celsius to Fahrenheit using short prompting. It shows a few example conversions to help guide the model before sending a new request: “Convert 37 Celsius to Fahrenheit.”

A prerequisite to convert Celsius to Fahrenheit

• Installed the required packages using the command pip install openai.
• While you can pass an API key as a keyword argument, it’s recommended to use python-dotenv and store your key in a .env file, such as: OPENAI_API_KEY=“My API Key”.
• Get an API key from your OpenAI account
• Ensure the OpenAI account has access to the “gpt-4o” model. If it doesn’t, you might want to switch to an alternative like “gpt-3.5-turbo.”

import os
from openai import OpenAI
# Initialize OpenAI client
client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY") # Use environment variable for API key
)
# Few-shot examples for temperature conversion
few_shot_examples = [
    {"role": "user", "content": "Convert 0 Celsius to Fahrenheit."},
    {"role": "assistant", "content": "32 Fahrenheit"},
    {"role": "user", "content": "Convert 100 Celsius to Fahrenheit."},
    {"role": "assistant", "content": "212 Fahrenheit"}
]
# Main query for temperature conversion
main_query = {"role": "user", "content": "Convert 37 Celsius to Fahrenheit."}
# Combine system message, few-shot examples, and main query
messages = [
    {"role": "system", "content": "You are a helpful assistant that converts temperatures from Celsius to Fahrenheit."}
] + few_shot_examples + [main_query]
# Create a chat completion request
completion = client.chat.completions.create(
    model="gpt-4o", # Specify GPT model
    messages=messages
)
# Print the response
print(completion.choices[0].message.content)

• First, the code sets up an OpenAI client using an API key stored in an environment variable. It also lays out a few-shot learning scenario with examples for temperature conversion.
• Next, it combines a system message, those few-shot examples, and a user query into a message list, which is meant to guide the model’s behavior.
• Finally, it calls the chat.completions.create method to send the messages to the gpt-4o model and prints out the assistant’s response.

Few-Shot Prompting with LangChain

LangChain is a user-friendly library designed to simplify working with large language models. It allows you to build chains, manage prompts, handle memory, and more. One of its powerful features is the FewShotPromptTemplate. It allows you to implement few-shot prompting without retraining the model.

Install LangChain
First, install LangChain using the command*:*

pip install langchain-openai

Set Up Few-Shot Prompting for Dictionary Definitions Let’s say you want to develop a prompt that guides the model to generate a dictionary-style definition for a specific word. You’ll provide a couple of examples and then request the definition for a new word.

from langchain.prompts import PromptTemplate, FewShotPromptTemplate

# Define a prompt template for individual examples
example_pr = PromptTemplate(
  input_variables=["word", "definition"],
  template="Word: {word}\nDefinition: {definition}\n"
)

# Create a list of example dictionaries
examples = [
  {"word": "Artificial intelligence", "definition": "Machines programmed to emulate human thought processes and decision-making abilities."},
  {"word": "Euphoria", "definition": "A powerful feeling of both happiness and excitement.."}
]

# Create the FewShotPromptTemplate with a prefix, the examples, and a suffix
prefix = "Provide a one-sentence definition for the following word."
suffix = "Word: {input}\nDefinition:"  # {input} will be the placeholder for our query word

few_shot_pr = FewShotPromptTemplate(
  examples=examples,
  example_prompt=example_pr,
  prefix=prefix + "\n\n",   # add spacing
  suffix=suffix,
  input_variables=["input"]
)

# Use the FewShotPromptTemplate to format a prompt for a new input
n_word = {"input": "Ephemeral"}
full_prompt = few_shot_pr.format(**n_word)
print(full_prompt)

Output:

Provide a one-sentence definition for the following word.

Word: Artificial intelligence
Definition: Machines programmed to emulate human thought processes and decision-making abilities.

Word: Euphoria
Definition: A powerful feeling of both happiness and excitement..

LangChain has smoothly incorporated our examples into a consistent format.

Pass Prompt to the LLM Using LangChain Now, you can pass full_prompt to the LLM to get the definition of the word Ephemeral.

import os
from langchain_openai import ChatOpenAI
os.environ["OPENAI_API_KEY"] = "your-api-key-here"
llm = ChatOpenAI(model="gpt-4")
response = llm.invoke(full_prompt)
print("\nGenerated Response:")
print(response)

Note: Ensure that your OpenAI API key has access to the specified model.

Best Practices for Effective Few-Shot Prompting

While few-shot prompting can improve the output of a large language model, getting it right can be challenging. Here are some effective prompt engineering techniques and best practices that will help maximize your few-shot setups.

Managing Token Count

• Why It Matters: Large prompts can exceed the limits of the model’s context window.
• Tip: Try to use shorter examples. If you have several examples that follow the same pattern, it’s better to summarize them instead of repeating each one.
• Token Count Management: Going over the token limit can cause the model to lose context from earlier examples, which may result in inconsistent responses.

Maintaining Prompt Clarity and Consistency

• Clear Task Definition: State exactly what you want the model to achieve.
• Consistent Formatting: If your examples follow a specific question-answer style, keep the new query in that same style.
• Avoid Over-Engineering: Adding too much unnecessary text can confuse the model’s understanding.

Choosing the Right Number of Shots

• Task Complexity: More complex tasks usually require more examples.
• Token Budget: Each extra example consumes more tokens, which can increase costs or hit limits.
• Empirical Testing: Try using 2-5 examples to see which works best—sometimes 3 or 4 hits the sweet spot.

Aligning Examples with the Task

• Relevant Scenarios: Provide examples that align closely with the type of input you expect.
• Include Challenging Examples: If possible, you can add some challenging examples to help the model learn how to handle tricky inputs.

Common Errors and How to Avoid Them

The following table displays an overview of frequent errors and their solutions when applying few-shot prompting.

Resolving them will help you build more reliable prompts for few-shot applications.

FAQ SECTION

What is few-shot prompting in NLP?
Few-shot prompting is a technique where you provide a few examples (typically 2 to 5) in a single query, allowing the large language model to understand how to tackle a new task. Instead of training the model separately, you teach it the task within the context.

How does few-shot prompting work in ChatGPT or GPT-4?
You provide examples and the new query in the same prompt. The model processes the entire prompt and uses those examples to respond to the new query.

What is the difference between zero-shot and few-shot prompting?

• Zero-Shot Prompting: Here, you provide a task description or instruction without any examples.
• Few-Shot Prompting: In this case, you include several examples in addition to your instruction. This usually leads to better accuracy and consistency.

When should I use few-shot prompting instead of fine-tuning?
Consider using few-shot prompting when you don’t have a large labeled dataset, need quick results without the hassle of training, or want to tackle multiple small tasks efficiently. Fine-tuning is a better fit when you have a substantial dataset and need reliable performance across a wide range of queries.

Are there best practices for designing few-shot prompts?

• Choose clear and representative examples.
• Maintain consistent formatting.
• Avoid mixing different tasks.
• Be mindful of the token limit.

Conclusion

Guiding large language models with few-shot prompting requires a small set of well-designed examples to achieve practical and efficient results. This method makes fine-tuning unnecessary but enables models to achieve high performance.

Few-shot prompting proves valuable in situations where data is limited or quick transitions between tasks are required.

Best practices such as using clear instructions, maintaining consistent formatting, and adhering to token limits will maximize effectiveness. The OpenAI API and LangChain make few-shot prompting easy to implement and scale.

Future iterations of LLMs will require developers to master this technique to build reliable, context-aware AI solutions.

Resources

To gain additional insights and expand your understanding, you can check out our related articles:

• TinyLlama
• Understanding Reasoning in LLMs
• LangChain Language Model
• Supervised vs Unsupervised Learning
• How to Implement Few-Shot Prompting(Beginner-Friendly Tutorial)
• Zero-Shot vs. Few-Shot Prompting: Choosing the Right Approach for Your AI Model
• Harness the Power of LLMs: Zero-shot and Few-shot Prompting
• 0-Shot vs Few-Shot vs Partial-Shot Examples in Language Model Learning
• Zero, One, and Few-Shot Prompting with Langchain and OpenAI LLMs
• Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot