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Scenario:

You want to understand multiple algorithms quickly—like sorting, searching, or graph traversal—but writing examples manually takes time. AI can generate algorithm examples with explanations and edge cases, helping you learn efficiently and interactively.

Step 0: Define Your Goal

Example: Learn and compare Bubble Sort, Merge Sort, and Quick Sort in Python.

Goal: Generate working examples, step-by-step explanations, and optionally test cases for each algorithm.

Step 1: Craft the AI Prompt

Treat AI as a coding tutor. Include:

1. Algorithm names or topics
2. Desired language (Python, JavaScript, etc.)
3. Optional: explanations, edge cases, example inputs/outputs

Example Prompt:

Generate Python examples for Bubble Sort, Merge Sort, and Quick Sort.
Include comments explaining each step and provide example inputs and outputs.

Step 2: AI Output Example (Condensed)

Bubble Sort:

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

print(bubble_sort([5,2,9,1]))  # [1,2,5,9]

Explanation: Compare neighbors, swap if needed, repeat until sorted.

Merge Sort:

def merge_sort(arr):
    if len(arr) <= 1:
        return arr
    mid = len(arr)//2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])
    result = []
    while left and right:
        if left[0] < right[0]:
            result.append(left.pop(0))
        else:
            result.append(right.pop(0))
    result += left + right
    return result

print(merge_sort([5,2,9,1]))  # [1,2,5,9]

Explanation: Divide array, recursively sort halves, merge sorted lists.

Quick Sort:

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    less = [x for x in arr[1:] if x <= pivot]
    greater = [x for x in arr[1:] if x > pivot]
    return quick_sort(less) + [pivot] + quick_sort(greater)

print(quick_sort([5,2,9,1]))  # [1,2,5,9]

Explanation: Choose pivot, partition, recursively sort partitions.

Step 3: Mini Lab Challenges

1. Ask AI to generate selection sort, insertion sort, and heap sort with explanations.
2. Generate search algorithms: linear search, binary search with step-by-step reasoning.
3. Request edge cases, e.g., empty lists, duplicate values, or negative numbers.
4. Challenge: Generate a comparison table showing time complexity for all generated algorithms.

Step 4: Pro Tips

• Ask AI to include comments, base cases, and example outputs
• Compare multiple algorithms side by side to understand differences and trade-offs
• Use AI examples to experiment interactively and modify inputs
• Iteratively explore more advanced variations: recursive vs iterative, space vs time optimization

Lab Summary

• AI accelerates learning by generating multiple algorithm examples with explanations
• Clear prompts + concept description = practical, interactive learning
• Experiment with edge cases and variations to master the logic behind algorithms
• Using AI in this way builds both understanding and coding skill efficiently