Beating LeetCode with Systematic Thinking: A Step-by-Step Framework for Coding Interviews

How to methodically dissect problems, avoid common pitfalls, and write solutions that impress interviewers.

You’ve been here before: staring at a LeetCode problem, unsure where to start. You brute-force an approach, get stuck optimizing, and panic as time ticks away. What if you could turn chaotic problem-solving into a repeatable process?

In this article, I’ll share the 4-step framework I used to solve LeetCode problems. No memorization is required—just systematic thinking.

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Step 1: Categorize the Problem

Goal: Identify the pattern in under 2 minutes by looking for key signals—input type, common keywords, and problem constraints.

1. Inspect the Input Type

• Arrays & Strings: Potential for sliding window, two pointers, prefix sums, or sorting strategies.

• Trees & Graphs: Look for DFS, BFS, topological sort, union-find (disjoint sets).

• Linked Lists: Often revolve around pointer manipulation (slow/fast pointers), reversing lists, or merging.

• Matrices/2D Grids: Typically DFS, BFS for “island” or “path” style problems, or dynamic programming for path optimizations.

2. Scan for Keywords

Certain words hint at certain algorithms or data structures. Use this quick-reference table to match keywords to likely patterns:

Example:

• “Longest substring without repeating characters” → Sliding Window

  • Reason: “substring” implies a contiguous portion of a string; O(n) time implies a single-pass approach with two pointers.

3. Consider Constraints (Time & Space)

• If the input size can be up to 10^5 or 10^6, an O(n^2) approach will be too slow; aim for O(n) or O(n log n).

• If space is limited (e.g., “constant extra space” or “in-place” requirement), solutions like in-place sorting or two-pointer swaps may be necessary.

• O(log n) time constraints often signal binary search or tree-based approaches.

Tip: The problem statement’s desired complexity (often hinted by constraints) narrows down possible algorithms. For instance, an O(n) or O(n log n) time requirement is a strong clue you shouldn’t be attempting an exhaustive backtracking or naive double loop.

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Step 2: The 4-Step Framework

1. Break It Down

Rephrase the problem in plain English.

• Bad: “I need to find the longest substring.”

• Good: “Track unique characters in a window, expand until duplicates appear, then shrink from the left.”

Identify Edge Cases:

• Empty input? All duplicates? Case sensitivity?

2. Visualize

Sketch scenarios (even for non-visual problems):

• Arrays: Draw pointers, partitions.

• Trees: Sketch recursion paths.

• Graphs: Map nodes and edges.

Example for LeetCode 124: Binary Tree Max Path Sum:

Copy

     -10  
     /  \  
    9    20  
        /  \  
       15   7  

Visual Insight: The max path could be 15 → 20 → 7 (sum 42), ignoring the root.

3. Optimize

Time-Space Tradeoffs:

• Replace nested loops with hash maps (e.g., two-sum).

• Use recursion with memoization for overlapping subproblems (DP).

• Precompute values (prefix sums, frequency counts).

Red Flags:

• Brute-force solutions with O(n²) time.

• Recursion without memoization (stack overflow risk).

4. Code

Interview-Ready Code Checklist:

• Descriptive variable names (left_ptr, max_sum).

• Handle edge cases upfront.

• Add brief comments for complex logic.

Bad vs Good Code:

# Bad: Unclear variables  
def f(s):  
    d = {}  
    l = 0  
    ...  

# Good: Self-documenting  
def longest_unique_substring(s: str) -> int:  
    char_index_map = {}  
    left = 0  
    max_length = 0  
    ...  

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Step 3: Avoid These 3 Deadly Traps

1. The “Code First, Think Later” Mistake

• Why It Fails: Jumping into code without planning leads to confusion, endless debugging, and missed edge cases.

• How to Fix It: Spend at least 10 minutes on problem analysis (Steps 1–3) before writing a single line of code. Outline your approach, consider edge cases, and confirm time/space constraints.

2. Overcomplicating Solutions

• Why It Fails: Using advanced data structures or algorithms when simpler ones suffice wastes time and increases bug risk.

• How to Fix It: Always ask, “What’s the simplest approach that meets the constraints?” Start with that and only optimize further if needed.

3. Ignoring Space Complexity

• Why It Fails: Focusing solely on time complexity can lead to memory bloat or stack overflow—problems you discover too late.

• How to Fix It: Calculate space complexity upfront. If recursion or extra data structures could get too large, switch to an iterative or more memory-efficient approach.

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Case Study: LeetCode 239 – Sliding Window Maximum

Problem Statement
You are given an integer array nums and an integer k, representing the size of a sliding window. For each valid window position, return the maximum value within that window.

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Step 1: Categorize the Problem

1. Identify the Input Type

   • Input is an array (nums) with the potential for repeated elements.

2. Look for Keywords

   • “Sliding Window,” “Maximum,” and “Size k.”

   • These keywords strongly hint at the Sliding Window pattern and the possibility of a deque (double-ended queue) solution to keep track of maximum elements.

3. Check Constraints

   • Typically, n can be large (e.g., up to 10^5).

   • A naive O(n*k) approach will be too slow for large n if k is also large.

   • This suggests we need an O(n) or O(n log n) approach.

Result: We’ve placed the problem into the Sliding Window category, where we keep track of the maximum using a deque or similar structure.

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Step 2: Use the 4-Step Framework

Now that we’ve categorized it, let’s apply the framework in detail.

1. Break It Down

• Plain English Restatement
  “We slide a window of size k across the array. For every window position, find the maximum element.”

• Edge Cases

  • k = 1: Return the array as-is (every element is its own max).

  • k = len(nums): Only one window; the result is a single value—the max of the entire array.

  • Mixed or Negative Values: Ensure we handle them correctly (the max may be negative).

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2. Visualize

To really see how this works, consider a concrete example:

nums = [1, 3, -1, -3, 5, 3, 6], k = 3

Window positions:
1) [1, 3, -1], -3, 5, 3, 6  -> max = 3
2) 1, [3, -1, -3], 5, 3, 6  -> max = 3
3) 1, 3, [-1, -3, 5], 3, 6  -> max = 5
4) 1, 3, -1, [-3, 5, 3], 6  -> max = 5
5) 1, 3, -1, -3, [5, 3, 6]  -> max = 6

• Naive Approach: For each window, scan k elements. This is O(n*k), which can be costly for large n.

• Key Insight: We can maintain a deque that stores indices of elements in descending order. The element at the front is always the window’s maximum.

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3. Optimize

• Time Complexity Goals

  • A direct approach: O(n*k) – too slow for large n and k.

  • Optimized approach: O(n), by ensuring each index enters and leaves the deque at most once.

• Why a Deque?

  • We can push new elements (by index) at the back.

  • Pop from the back any elements smaller or equal to the new element (they’ll never be needed).

  • Pop from the front if the front index is out of the current window (i.e., front == i - k).

  • The front of the deque is always the index of the current window’s maximum element.

Result: Each element is processed in constant time, leading to a total of O(n).

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4. Code

Below is an interview-ready Python solution. Note the inline comments that map back to our strategy:

from collections import deque
from typing import List

def max_sliding_window(nums: List[int], k: int) -> List[int]:
    # Handle trivial case
    if k == 1:
        return nums
    
    q = deque()  # will store indices of potential maxima
    result = []
    
    for i, num in enumerate(nums):
        # 1. Pop from the back while the current num is >= the element at q[-1]
        #    Those smaller elements can't be a future max
        while q and nums[q[-1]] <= num:
            q.pop()
        
        # 2. Push the current index onto the back
        q.append(i)
        
        # 3. If the front of the deque is out of the window, pop it
        if q[0] == i - k:
            q.popleft()
        
        # 4. Once we've processed at least k elements, the front of q is the max
        if i >= k - 1:
            result.append(nums[q[0]])
    
    return result

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Mental Models from Top Engineers

1. The 20-Minute Rule

   • What It Is: Spend no more than 20 minutes stuck on a single approach. If you’re still spinning your wheels, seek a hint or switch tactics.

   • Why It Works: Timeboxing prevents you from wasting hours on a dead end and encourages you to explore alternative methods sooner.

2. Spaced Repetition

   • What It Is: Revisit problems and patterns weekly to consolidate learning (tools: Anki, LeetCode’s “Review” feature).

   • Why It Works: The human brain retains information more effectively when it’s reinforced at structured intervals rather than cramped.

3. Mock Interviews

   • What It Is: Practice out loud with friends or online platforms (e.g., Pramp, Interviewing.io).

   • Why It Works: Verbalizing solutions reveal gaps in your reasoning and help you simulate real interview pressure.

4. The Feynman Technique

   • What It Is: Teach or explain a problem in your own words as if you’re instructing a beginner.

   • Why It Works: Breaking down complex ideas into simple explanations clarifies your thought process and uncovers hidden assumptions.

5. “Post-Mortem” Analysis

   • What It Is: After solving each problem (or failing to), jot down what went well, where you struggled, and how you’d improve next time.

   • Why It Works: Reflection cements lessons learned and highlights recurring mistakes, so you don’t repeat them.

6. The Pareto Principle (80/20 Rule)

   • What It Is: Focus on the 20% of problem types or patterns (e.g., sliding window, DFS, DP) that appear 80% of the time in interviews.

   • Why It Works: Targeted practice on the most common patterns yields the biggest bang for your buck in a limited timeframe.

7. Rubber Duck Debugging

   • What It Is: Explain each line of your logic to an inanimate “listener” (like a rubber duck) or even just yourself.

   • Why It Works: Speaking out loud forces you to slow down and identify subtle errors or assumptions in your reasoning.

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Your Action Plan

1. Learn Patterns, Not Problems: Focus on categories (e.g., sliding window, DFS).

2. Time Yourself: Solve easies in 10 minutes, mediums in 20, hards in 30.

3. Analyze Failures: For every wrong answer, write down why (e.g., missed edge case).

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Final Tip: Embrace the Process, Not Just the Answer

LeetCode success isn’t about raw talent or memorizing a hundred solutions—it’s about using a consistent, repeatable framework. The next time you face a challenging problem:

1. Categorize the question type (arrays, graphs, DP, etc.).

2. Visualize the steps or data structures involved.

3. Optimize with an eye on time and space constraints.

4. Code cleanly, handling edge cases upfront.

By shifting from guesswork to structured problem-solving, you’ll tackle even the toughest interviews with confidence. More importantly, these skills carry over to real-world engineering, turning every coding challenge into a stepping stone for your growth.

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