AWS Certified Cloud Practitioner

Explanation

Few-shot prompting is the correct technique because:

1. Definition: Few-shot prompting involves providing the model with a few examples (typically 2-5) of input-output pairs to demonstrate the desired pattern or format before asking it to generate a response for a new input.

2. Why it fits the scenario: The team wants to "show a few example inputs and expected outputs within the prompt" to help the model produce consistent outputs. This is exactly what few-shot prompting does - it gives the model concrete examples to learn from.

3. Comparison with other options:

   • Zero-shot prompting (A): The model is asked to perform a task without any examples. This wouldn't provide the consistency the team is looking for.

   • Chain-of-thought prompting (C): This technique asks the model to explain its reasoning step-by-step, which is useful for complex reasoning tasks but not specifically for providing example patterns.

   • Self-reflection prompting (D): This involves asking the model to critique or improve its own responses, which is different from providing example patterns.

4. Practical application: In Amazon Bedrock, few-shot prompting would involve structuring the prompt like:

   
   Example 1:
   
   Input: "Show me all customers from New York"
   
   Output: "SELECT * FROM customers WHERE city = 'New York'"
   
   
   
   Example 2:
   
   Input: "Find orders placed in January 2024"
   
   Output: "SELECT * FROM orders WHERE order_date BETWEEN '2024-01-01' AND '2024-01-31'"
   
   
   
   Now generate SQL for: "Get products with price over `$100`"
   
   

   
   Example 1:
   
   Input: "Show me all customers from New York"
   
   Output: "SELECT * FROM customers WHERE city = 'New York'"
   
   
   
   Example 2:
   
   Input: "Find orders placed in January 2024"
   
   Output: "SELECT * FROM orders WHERE order_date BETWEEN '2024-01-01' AND '2024-01-31'"
   
   
   
   Now generate SQL for: "Get products with price over `$100`"
   
   

This approach helps the model understand the specific format and style the team wants for SQL queries, leading to more consistent and accurate outputs.