Designing a Multi-Layered Prompt for Adaptive Code Suggestions - Sample

Designing a Multi-Layered Prompt for Adaptive Code Suggestions

A multi-layered prompt involves designing a sequence of conditional, contextual, and iterative prompts that adapt based on the user's needs. The prompt layers interact to refine, optimize, and enhance the code generation process.

Steps to Design a Multi-Layered Prompt

1. Define the Layers and Their Roles:

   • Layer 1 (Contextual Prompting): Understand the user’s intent and gather information about the coding task.
   • Layer 2 (Conditional Prompting): Adapt the code based on specific conditions or user preferences.
   • Layer 3 (Iterative Prompting): Refine the output through multiple iterations, offering enhancements or debugging as needed.

2. Design the Flow of the Prompt:

   • Start by asking the user for the task they want to achieve.
   • Based on the input, select the appropriate code template or strategy (e.g., simple function, optimized version, or advanced implementation).
   • Continue adapting based on feedback and offer iterative improvements.

3. Implement Logic to Switch Between Layers:

   • Use logical checks or decision trees to guide the flow between layers.
   • Allow user feedback to trigger refinement or enhancement processes.

Sample Project: Adaptive Code Generator for Sorting Algorithms

Let’s create a Python-based project where a multi-layered prompt generates code for sorting algorithms, adapting based on the user’s context and needs.

Layer 1: Contextual Prompting

Objective: Determine the user’s goal and gather input.

Prompt:

• “What type of sorting algorithm do you need? (Options: Bubble Sort, Quick Sort, Merge Sort)”
• “Do you prefer a basic implementation, optimized code, or an advanced version with additional features (e.g., performance metrics)?”

User Input Example:

• “Quick Sort, with performance metrics.”

Layer 2: Conditional Prompting

Objective: Generate the code based on the user’s choice.

Conditional Logic:

• If the user requests a basic implementation, provide a standard Quick Sort.
• If the user requests an optimized implementation, provide an in-place Quick Sort.
• If the user requests an advanced implementation with metrics, add performance measurements.

Sample Python Code (Advanced Implementation with Metrics):

python code
import time

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quick_sort(left) + middle + quick_sort(right)

def quick_sort_with_metrics(arr):
    start_time = time.time()
    sorted_arr = quick_sort(arr)
    end_time = time.time()
    execution_time = end_time - start_time
    print(f"Execution Time: {execution_time:.6f} seconds")
    return sorted_arr

# User input
arr = [12, 4, 5, 6, 7, 3, 1, 15]
sorted_arr = quick_sort_with_metrics(arr)
print("Sorted Array:", sorted_arr)

Layer 3: Iterative Prompting

Objective: Refine the code based on feedback, offering further optimizations or debugging help.

Iterative Process:

• User Feedback: “The code is good, but can you optimize memory usage?”
• Iteration 1: Suggest an in-place Quick Sort to reduce space complexity.

Updated Code:

python code
def quick_sort_in_place(arr, low, high):
    if low < high:
        pivot_index = partition(arr, low, high)
        quick_sort_in_place(arr, low, pivot_index - 1)
        quick_sort_in_place(arr, pivot_index + 1, high)

def partition(arr, low, high):
    pivot = arr[high]
    i = low - 1
    for j in range(low, high):
        if arr[j] <= pivot:
            i += 1
            arr[i], arr[j] = arr[j], arr[i]
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

# User input
arr = [12, 4, 5, 6, 7, 3, 1, 15]
quick_sort_in_place(arr, 0, len(arr) - 1)
print("Sorted Array:", arr)

Project Work: Implementing the Multi-Layered Prompt in Python

Project Objective: Build a Python-based command-line tool that takes user inputs, adapts based on context, and offers different versions of sorting algorithms.

1. Project Structure:

   • main.py: The main program that handles user input and logic.
   • sorting_algorithms.py: A module containing the different sorting algorithms.
   • metrics.py: A module for calculating performance metrics.

2. Step-by-Step Implementation:

   • Start by capturing user input for the algorithm type and implementation preference.
   • Based on the input, call the appropriate function from sorting_algorithms.py.
   • If metrics are requested, use functions from metrics.py to measure performance.
   • Implement an interactive loop that allows the user to request further optimizations or additional features.

3. Sample main.py:

python code
from sorting_algorithms import bubble_sort, quick_sort, merge_sort, quick_sort_with_metrics
from metrics import measure_performance

def get_user_input():
    print("Choose a sorting algorithm: Bubble Sort, Quick Sort, Merge Sort")
    algorithm = input("Algorithm: ").strip().lower()
    print("Choose implementation: Basic, Optimized, Advanced")
    implementation = input("Implementation: ").strip().lower()
    return algorithm, implementation

def main():
    algorithm, implementation = get_user_input()
    
    if algorithm == "quick sort":
        if implementation == "basic":
            arr = [int(x) for x in input("Enter numbers separated by space: ").split()]
            sorted_arr = quick_sort(arr)
            print("Sorted Array:", sorted_arr)
        elif implementation == "advanced":
            arr = [int(x) for x in input("Enter numbers separated by space: ").split()]
            sorted_arr = quick_sort_with_metrics(arr)
            print("Sorted Array:", sorted_arr)
    
    # Add similar logic for Bubble Sort and Merge Sort with different implementations

if __name__ == "__main__":
    main()

4. Running the Project:
   • Execute the script in the command line.
   • Provide inputs as prompted, and observe how the script adapts based on your choices.
   • Experiment with feedback and request enhancements to see how the layers interact.

Key Learnings:

• The multi-layered approach enables highly adaptive code suggestions, offering context-specific outputs while allowing room for refinement.
• By organizing the logic into layers, the system remains flexible, handling both simple and complex scenarios with ease.
• You can extend this framework to other coding tasks like API integrations, data analysis, or even full-stack applications.

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This project demonstrates how to design and implement a multi-layered prompt for adaptive Python code generation. It balances user intent with conditional logic and iterative improvements to deliver tailored, context-aware solutions.