Implement few-shot learning

Provide the model with a few examples of the desired output to guide it to produce responses that closely align with your expectations.

What is few-shot learning

Few-shot learning involves including a small number of input-output examples (usually between 1 to 5) within your prompt to demonstrate the task you want the model to perform. This approach helps the model understand the pattern or format you’re seeking, effectively “teaching” it how to generate the desired output without the need for extensive training data or fine-tuning.

How to implement few-shot learning

1. Provide clear examples
2. Separate the example from the prompt using a delimiters (For example, use lines like --- or phrases like Example: to separate sections).
3. Keep examples concise
4. Use examples that are reflective of desired outputs

Examples

Example 1: Email Response Generation.

The examples show the assistant how to structure the responses, the tone to use, and how to address the customer’s specific concerns.

Prompt:

You are an assistant helping to draft professional email responses.

Example 1:
Customer Inquiry: "I am interested in your software but have some questions about pricing."
Response: "Dear [Customer Name], thank you for reaching out. I'd be happy to provide more details about our pricing plans..."

Example 2:
Customer Inquiry: "Can I schedule a demo of your product?"
Response: "Hello [Customer Name], we'd be delighted to arrange a demo for you. Please let us know your availability..."

Now, based on the customer's message below, compose an appropriate response.

Customer Inquiry: "I'm experiencing issues with logging into my account. Can you assist?"
Response:

Example 2: Data Extraction.

The model learns to identify and extract specific pieces of information consistently across different job postings.

Prompt:

Extract key information from the following job postings.

Example:
Job Posting: "We are seeking a software engineer with 5 years of experience in Java and Python. Location: New York."
Extracted Information:
- Position: Software Engineer
- Experience: 5 years
- Skills: Java, Python
- Location: New York

Job Posting: "Looking for a marketing manager skilled in SEO and content creation. Must have at least 3 years of experience. Location: Remote."
Extracted Information:
- Position: Marketing Manager
- Experience: 3 years
- Skills: SEO, Content Creation
- Location: Remote

Now, process the following job posting.

Job Posting: "Wanted: Graphic designer proficient in Adobe Suite and illustration. Experience: 2 years minimum. Location: San Francisco."
Extracted Information:

Example 3: Sentiment Analysis.

The model learns to classify sentiments based on the examples provided, improving accuracy in its analysis.

Prompt:

Determine the sentiment (Positive, Negative, Neutral) of the following customer reviews.

Example 1:
Review: "The product quality is outstanding and exceeded my expectations."
Sentiment: Positive

Example 2:
Review: "I'm disappointed with the customer service I received."
Sentiment: Negative

Now analyze the following review.

Review: "The delivery was on time, but the packaging was damaged."
Sentiment:

Example 4: Style imitation in writing.

By providing examples, the model understands the style and themes characteristic of Einstein’s quotes, enabling it to generate a similar statement.

Prompt:

Write a motivational quote in the style of Albert Einstein.

Example 1:
"Life is like riding a bicycle. To keep your balance, you must keep moving."

Example 2:
"Imagination is more important than knowledge. Knowledge is limited; imagination encircles the world."

Now, generate a new motivational quote in the style of Albert Einstein.

Tips for effective few-shot learning

1. Use relevant and high-quality examples. Accuracy matters since incorrect examples can mislead the model. Make sure examples are clear and free of errors.
2. Maintain consistency in formatting. Uniform structure: Consistent formatting helps the model recognize patterns. Use the same separators or markers throughout.
3. Limit the number of examples. Be mindful of the model’s context window (maximum token limit). Often, 1-3 examples are enough to guide the model effectively.
4. Position examples strategically. Place examples before the main task instruction. Use phrases like “Now,” “Based on the above,” or “Your turn” to signal the shift to the new task.