Few Shot Prompting

In the previous article, we looked at a list of 10 popular prompting techniques and took a deep dive into zero-shot prompting. Let’s take a look at the next promoting technique, few-shot prompting.

Few-shot prompting is a technique used in natural language processing and machine learning, particularly with language models like GPT-3, where the model is given a few examples (typically between 2-5) to learn from before making predictions on new data. This method leverages the model’s pre-trained knowledge and its ability to generalise from a few examples rather than needing extensive fine-tuning with large labeled datasets.

History

The technique was first formally introduced and demonstrated at scale in the paper: “Language Models are Few-Shot Learners” by Brown et al. (2020) This paper, which introduced GPT-3, showcased the model’s ability to perform various tasks with just a few examples in the prompt.

How Few-Shot Prompting Works

  • Selection of Examples: A small number of representative examples are chosen. These examples should ideally cover the variations and complexities of the task.
  • Prompt Creation: A prompt is crafted that includes these examples as a way to ‘instruct’ the model on what is expected. This prompt effectively serves as a mini training session for the model, giving it context and guidance.
  • Inference: After the examples, the actual query or task is presented in the same format. The model then applies the patterns and rules it inferred from the examples to generate a prediction or response.

Examples of Few-Shot Prompting

Example 1: Sentiment Analysis

Prompt

Example 1:
Input: "This movie was absolutely fantastic! I loved every minute of it."
Output: Positive

Example 2:
Input: "I was thoroughly disappointed. The plot was confusing and the acting was terrible."
Output: Negative

Example 3:
Input: "While it had its moments, overall the film fell flat for me." 
Output: Negative

New Input: "I can't wait to see this movie again! The special effects were mind-blowing."
Expected Output: Positive

Example 2: Translation

Prompt: 

Translate the following English sentences to French:
English: "Hello, how are you?" French: "Bonjour, comment ça va?"
English: "What is your name?" French: "Comment vous appelez-vous?"

Translate the following sentence:
English: "I am learning to speak French." Model's Response: "Je apprends à parler français."

Benefits of few-shot prompting

  • Allows models to quickly adapt to new tasks without fine-tuning
  • Reduces the need for large labeled datasets
  • Enables zero-shot and one-shot learning capabilities
  • Improves performance on tasks with limited training data

Use Case: Revolutionising Customer Support with Few-Shot Prompting

Let’s explore how few-shot prompting can transform customer support in the bustling world of e-commerce.

Customer Support Automation at Scale in an E-commerce Company

Challenge: Meet Random Retail Co., an e-commerce giant swamped with customer queries. The demand for personalised responses led to long wait times and customer dissatisfaction as the support team struggled to keep up.

GenAI Powered Solution: Enter Few-Shot prompting! Random Retail Co. harnessed the power of this technique to supercharge its AI-driven customer support. By training their AI with just a handful of examples, they enabled it to generate accurate, contextually relevant responses across a wide range of inquiries.

Implementation Steps:

  • Identify Common Queries: Analyse historical customer support data to uncover the most frequent inquiries. Categorise these queries into themes such as account issues, product information, technical support, and billing.
  • Select Representative Examples: Choose a small number of examples (typically 2-5) for each category. Ensure these examples capture the variations and complexities within each query type.
  • Create Prompts: Craft prompts that include these representative examples, providing clear context and guidance for the AI.
Example 1:
Input: "I can't log into my account. Can you help me reset my password?"
Output: "Sure! To reset your password, please follow this link: [reset link] and follow the instructions."

Example 2:
Input: "I forgot my username. How can I recover it?"
Output: "You can recover your username by visiting our account recovery page here: [recovery link] and entering your registered email address."

New Input: "I am unable to access my account after multiple login attempts. What should I do?"
  • Integrate with AI Model: Use the crafted prompts to train the AI model. Ensure the AI can infer patterns and generate responses based on the few-shot examples provided.
  • Test and Validate: Conduct extensive testing to validate AI-generated responses. Use a mix of historical and new queries to ensure accuracy and relevance. Iterate on the prompts and examples based on feedback to fine-tune performance.
  • Deploy and Monitor: Deploy the AI-enhanced customer support system. Monitor performance in real time, focusing on metrics like response time, customer satisfaction scores, and query resolution rates. Continuously gather feedback to identify areas for improvement.
  • Scale and Optimise: Gradually scale the system to handle more types of inquiries and complex scenarios as the AI model proves successful. Optimise the AI with additional examples and updated prompts based on ongoing analysis and feedback.

Happy prompting!