Advanced Meta Prompting Strategies

Below are use cases for each of the advanced meta prompting strategies (layering prompts, multi-agent prompting, and dynamic role assignment) specifically applied to Python code generation.

1. Layering Prompts: Combining Multiple Meta-Prompting Types

Concept: This strategy involves creating a multi-layered prompt that incorporates several types of meta-prompting, such as conditional, iterative, and contextual prompting, to deliver a refined and complex output.

Use Case: Adaptive Python Code Generation for a Web Scraping Script Imagine you’re designing an AI prompt that helps users generate Python code for web scraping. The goal is to adjust the code based on the user’s requirements: whether they want basic scraping, error handling, or advanced features like data extraction and storage.

Meta Prompt Structure:

• Layer 1 (Contextual Prompting): "What is the purpose of the web scraping? Basic data extraction, error-resilient scraping, or advanced automation?"
• Layer 2 (Conditional Prompting): Depending on the response, the prompt generates either simple code, adds error handling, or implements advanced techniques like data parsing and storage.
• Layer 3 (Iterative Prompting): If the user asks for more features or improvements, the code is refined, iteratively adding functionalities such as retry mechanisms or customizable headers.

Scenario:

1. User Input: "I want a script to scrape titles from a webpage."
2. Generated Code (Basic):

   python code
   import requests
   from bs4 import BeautifulSoup
   
   url = "https://example.com"
   response = requests.get(url)
   soup = BeautifulSoup(response.text, "html.parser")
   titles = [title.get_text() for title in soup.find_all("h1")]
   print(titles)

3. User Request for Improvements: "Can you add error handling and retries?"
4. Refined Code (Advanced):

   python code
   import requests
   from bs4 import BeautifulSoup
   from requests.exceptions import RequestException
   import time
   
   def fetch_page(url, retries=3):
       for i in range(retries):
           try:
               response = requests.get(url, timeout=10)
               response.raise_for_status()
               return response
           except RequestException as e:
               print(f"Error: {e}. Retrying... ({i+1}/{retries})")
               time.sleep(2)
       return None
   
   url = "https://example.com"
   response = fetch_page(url)
   if response:
       soup = BeautifulSoup(response.text, "html.parser")
       titles = [title.get_text() for title in soup.find_all("h1")]
       print(titles)
   else:
       print("Failed to retrieve webpage.")
   

2. Multi-Agent Prompting: Using Different "Roles" or "Agents" for Various Aspects of a Task

Concept: This strategy involves dividing the task into different roles or agents, each handling

 a specific aspect of the code generation process.

Use Case: Collaborative Python Code Generation with Specialized Agents Imagine a scenario where the prompt uses multiple agents, each focusing on specific concerns like code structure, optimization, and documentation.

Meta Prompt Structure:

• Agent 1 (Code Architect): Focuses on the overall structure and design of the code.
• Agent 2 (Performance Optimizer): Refines the code for performance and efficiency.
• Agent 3 (Documentation Specialist): Generates comprehensive comments and docstrings for the code.

Scenario:

1. User Input: "I need a Python function to calculate the nth Fibonacci number using dynamic programming."
2. Agent 1 - Code Architect:
   • Generates the initial structured code.

   python code
   def fibonacci(n):
       dp = [0] * (n + 1)
       dp[1] = 1
       for i in range(2, n + 1):
           dp[i] = dp[i-1] + dp[i-2]
       return dp[n]
   

3. Agent 2 - Performance Optimizer:
   • Suggests a space-optimized version.

   python code
   def fibonacci(n):
       if n <= 1:
           return n
       a, b = 0, 1
       for _ in range(2, n + 1):
           a, b = b, a + b
       return b
   

4. Agent 3 - Documentation Specialist:
   • Adds comments and a docstring.

   python code
   def fibonacci(n):
       """
       Calculate the nth Fibonacci number using an iterative approach.
       
       Args:
           n (int): The position of the Fibonacci number to calculate.
       
       Returns:
           int: The nth Fibonacci number.
       """
       if n <= 1:
           return n
       a, b = 0, 1
       for _ in range(2, n + 1):
           a, b = b, a + b
       return b
   

3. Dynamic Role Assignment: Adjusting the Prompt’s Objective Mid-Conversation

Concept: The prompt dynamically switches objectives based on user feedback or evolving requirements, allowing the generation process to pivot as needed.

Use Case: Evolving Code Requirements for a Python Data Analysis Script Imagine you’re working with a prompt that dynamically adjusts between code generation, debugging, and enhancement based on user requests.

Meta Prompt Structure:

• Initial Role: Start with generating basic code.
• Pivot to Debugging: If issues arise, the prompt pivots to debugging.
• Pivot to Enhancement: After basic functionality is confirmed, the prompt focuses on optimization or adding new features.

Scenario:

1. User Input: "Generate a Python script to analyze a CSV file and find the average of a column."
2. Initial Role - Code Generation:

   python code
   import pandas as pd
   
   def calculate_average(csv_file, column_name):
       df = pd.read_csv(csv_file)
       return df[column_name].mean()
   
   # Usage
   average = calculate_average('data.csv', 'column_name')
   print(f'Average: {average}')
   

3. User Feedback: "I’m getting an error when the column contains non-numeric data."
4. Pivot to Debugging:

   python code
   import pandas as pd
   
   def calculate_average(csv_file, column_name):
       df = pd.read_csv(csv_file)
       # Handle non-numeric values
       numeric_data = pd.to_numeric(df[column_name], errors='coerce')
       return numeric_data.mean()
   
   # Usage
   average = calculate_average('data.csv', 'column_name')
   print(f'Average: {average}')
   

5. User Request for Enhancements: "Can you also add support for handling missing values and a summary of the column?"
6. Pivot to Enhancement:

   python code
   import pandas as pd
   
   def calculate_average(csv_file, column_name):
       df = pd.read_csv(csv_file)
       # Handle non-numeric values and missing data
       numeric_data = pd.to_numeric(df[column_name], errors='coerce')
       mean_value = numeric_data.mean()
       summary = df[column_name].describe()
   
       print("Column Summary:")
       print(summary)
       return mean_value
   
   # Usage
   average = calculate_average('data.csv', 'column_name')
   print(f'Average: {average}')
   

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These use cases illustrate how advanced meta-prompting strategies can adapt Python code generation to meet evolving requirements, manage complex scenarios, and produce high-quality outputs tailored to specific coding contexts.