Dominate LLMs: The Ultimate Guide to Few-Shot Prompting Techniques

Few-shot prompting is rapidly changing how we interact with natural language processing. It empowers you to get powerful, tailored results without the cumbersome process of fine-tuning. But what is it, and how can you use it effectively?

This guide dives deep into the world of few-shot prompting, offering actionable strategies and real-world examples to help you master this essential technique.

Unlock the Power of Few-Shot Learning

Few-shot prompting gives a language model a small number of input-output examples ("shots") directly in the prompt. Instead of retraining the model, you show it how to perform the task.

The model recognizes patterns from the examples and applies them to new, unseen queries. This lets it infer how to handle similar tasks without any modifications to its internal settings like you normally would by fine-tuning.

In-Context Learning: The Engine Behind Few-Shot Prompting

In-context learning is what makes few-shot prompting work so well. Here's how:

• Sequence Modeling: The model sees the entire prompt—examples + your query—as one continuous sequence.
• Pattern Extraction: It finds patterns from the examples to figure out how to respond to your new query.
• Prediction: Using its pre-existing knowledge and the extracted patterns, it predicts the most likely answer to your query.

The best part? No need for any complex adjustments or parameter changes, it all happens through the prompt.

Zero-Shot vs. One-Shot vs. Few-Shot: Choosing the Right Approach

Understanding the different types of prompting is crucial for optimal results:

• Zero-Shot Prompting: Give the model direct instructions without any examples. Example: "Translate this sentence from French to English: 'Bonjour le monde'." Use it for quick, simple tasks where the model likely already knows the answer.
• One-Shot Prompting: Provide one input-output example before the actual request. Example: "Translate: 'Salut' → 'Hello'. Now translate: 'Bonjour' → ?" Good when the model needs a specific format but the task is straightforward.
• Few-Shot Prompting: Show the model several examples to teach it the desired pattern. Example: Multiple translation examples followed by a new sentence to translate. Ideal for nuanced or complex tasks where the model benefits from multiple demonstrations.

Each prompting strategy balances task complexity with the context the model needs.

Real-World Few-Shot Prompting Examples

Dive into these practical examples to see few-shot prompting in action, boosting outcomes using LLMs across multiple applications.

Text Classification: Sentiment Analysis

Help the language model classify text sentiment by providing labeled examples:

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:

The two examples teach the model the format ("Review: ... Sentiment: ...") and how to distinguish between positive and negative language. The model then correctly identifies the third review as Positive.

Concise Text Summarization

Control the summarization style by providing example article-summary pairs. This helps the model in learning the pattern, achieving more value and relevance across outcomes.

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:

Python Code Generation

Generate Python code from pseudocode by providing a few examples. This is especially helpful for teaching models about code syntax and logic.

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

Few-Shot Prompting: Practical Use with GPT-4, ChatGPT, and Claude

Few-shot prompting principles remain consistent across leading platforms:

• GPT-4 / ChatGPT: Use system messages or user prompts with clear examples. GPT-4 excels due to its superior language understanding.
• Claude (Anthropic): Provide a conversation or context with examples to guide the response style and format with Claude.

Code implementation with OpenAI API showcasing temperature conversion

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: consider "gpt-3.5-turbo" if you do not have access
    messages=messages
)

# Print the response
print(completion.choices[0].message.content)

This script sets up an OpenAI client, provides temperature conversion examples, and sends a query to the gpt-4o model. The few-shot examples guide the model's behavior, resulting in an accurate response.

Streamline Few-Shot Prompting with LangChain

LangChain simplifies LLM interactions, offering tools to build chains, manage prompts, and more.

Create a Few-Shot Prompt for Dictionary Definitions

Use LangChain's FewShotPromptTemplate to guide the model in generating dictionary-style definitions:

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)

Process the Prompt through the LLM in LangChain

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)

Best Practices for Few-Shot Prompt Engineering

Optimize your few-shot setups with these prompt engineering techniques:

• Managing Token Count: Use shorter examples. Compress and summarize examples fitting a pattern instead of repeating.
• Maintaining Prompt Clarity and Consistency: Define the task clearly. Maintain a consistent formatting style. Avoid unnecessary details.
• Choosing the Right Number of Shots: Complex tasks need more examples. Test with 2-5 examples to find the sweet spot.
• Aligning Examples with the Task: Use relevant examples that closely match expected inputs. Include challenging examples when possible.

Avoid These Common Few-Shot Prompting Mistakes

FAQ: Mastering Few-Shot Prompting

• What is few-shot prompting in NLP? A technique where you provide a few examples, that enable the large language model to understand how to tackle a new task efficiently.
• How does few-shot prompting work in ChatGPT or GPT-4? You give examples and the new query in the same prompt. The model uses the examples to guide its response.
• What is the difference between zero-shot and few-shot prompting? Zero-shot prompting uses no examples, while few-shot prompting uses several examples in addition to your instruction.