How to Explain LeetCode Solutions in Plain English for Interview Success

Source: Dev.to

Introduction

Got the code right but struggled to explain your approach? Learn the exact framework for translating algorithms into clear, confident verbal explanations that impress interviewers.

The code works. You submit it. “Accepted.”

But in your mock interview, when asked to explain your solution, you freeze. You stare at your code and mumble: “Um… so this loop… it like… goes through the array… and then… checks stuff…”

Your interviewer waits. Silence.

The problem: You solved it, but you can’t translate your code into coherent English. In real interviews, communication is just as important as correctness.

This guide teaches you a step‑by‑step framework for converting your LeetCode solutions into clear, confident verbal explanations that demonstrate both technical depth and communication skill.

TL;DR

• Interviews test two skills: solving the problem and explaining your thinking clearly.
• Most candidates can code but struggle to verbalize their approach without rambling or getting lost in details.
• The framework:
  1. State the problem in your own words
  2. Describe the high‑level strategy
  3. Explain why it works
  4. Walk through a concrete example
  5. Analyze complexity
• Common mistake: narrating code line‑by‑line instead of explaining the core insight and algorithmic approach.
• Practice technique: write your solution, then explain it out loud without looking at the code. Record yourself to catch filler words and unclear explanations.
• You’ll learn: how to structure verbal explanations, what to emphasize, how to use examples effectively, and communication habits that signal confidence.

Beginner‑Friendly Explanations

Why Explaining is Harder Than Coding

When you code, you’re having a conversation with the compiler. When you explain, you’re having a conversation with a human who may or may not follow your logic.

Coding

• Precise syntax
• Sequential execution
• Compiler catches errors

Explaining

• Natural language (imprecise, ambiguous)
• Non‑linear reasoning (“here’s the idea, now let me show you an example, now back to the algorithm”)
• Interviewer’s understanding varies

Many strong coders struggle because they never practiced the translation layer between “what my code does” and “why my approach works.”

The Two‑Level Explanation Model

Level 1 – The Strategy (High‑Level)

• What’s the core idea?
• What pattern or technique are we using?
• Why does this approach solve the problem?

Example: “We’ll use a sliding window to track the longest substring without repeating characters. As we expand the window, if we hit a duplicate we shrink from the left until it’s valid again.”

Level 2 – The Mechanics (Implementation Details)

• How do we track state? (data structures)
• What’s the loop structure?
• How do we handle edge cases?

Example: “We’ll use a hash set to store characters in the current window. Two pointers, left and right. right expands the window; if we find a duplicate, left shrinks it by removing characters from the set until the duplicate is gone.”

Tip: Always start with Level 1 (strategy) before diving into Level 2 (mechanics). Jumping straight to mechanics confuses the interviewer because they lack the mental scaffold to follow the details.

What Interviewers Are Actually Listening For

1. Clarity – Can they follow your reasoning without getting lost?
2. Structure – Do you organize your thoughts logically?
3. Insight – Do you understand why your solution works, or did you just memorize a pattern?
4. Communication – Can you adapt your explanation if the interviewer looks confused?

Understanding how to explain your thought process is a meta‑skill that applies beyond solution walkthroughs—it shapes how you approach the entire interview.

Step‑by‑Step Learning Guidance

Step 1: Restate the Problem in Your Own Words

Template

“So we need to [goal], given [inputs]. The constraints are [notable limits]. Let me clarify: [edge case or ambiguity].”

Example

“We need to find the longest substring without repeating characters, given a string. The string can be up to 10,000 characters. Just to clarify: does ‘repeating’ mean any duplicate, or consecutive duplicates?”

Why it works: Shows comprehension and gives the interviewer a chance to correct misunderstandings before you dive into implementation.

Step 2: State Your High‑Level Strategy First

Bad (too detailed too fast)

“I’m going to use a hash set and two pointers, one at the start and one that moves right, and when I hit a duplicate I’ll remove from the set…”

Good (strategic first)

“I’ll use a sliding‑window approach. The idea is to expand a window to the right as long as all characters are unique, and shrink it from the left when we encounter a duplicate.”

Template

“I’ll use [pattern/technique]. The core idea is [one‑sentence strategy].”

Step 3: Explain Why Your Approach Works

Example

“This works because at any moment the substring between left and right is guaranteed to have no duplicates. By shrinking from the left when we find a duplicate, we maintain that invariant while checking every possible valid substring.”

Template

“This works because [fundamental principle]. By doing [X], we ensure [correctness guarantee].”

Step 4: Walk Through a Concrete Example

Problem: Longest Substring Without Repeating Characters
Input: "abcabcbb"

Walkthrough

“Start with both pointers at index 0. Expand right: a, ab, abc – all unique, so max = 3. Next character is another a; duplicate found. Shrink from the left, removing the first a; window becomes bca. Continue expanding. When we hit the second b, shrink again, and so on. The largest window we ever saw was length 3, which is the answer.”

Why it works: Concrete examples make abstract algorithms tangible; interviewers can visualize the process.

Step 5: Mention Time and Space Complexity

Template

“The time complexity is O([expression]) because [reason]. The space complexity is O([expression]) because [reason].”

Example

“Time complexity is O(n) because we visit each character at most twice—once when expanding right, once when shrinking left. Space complexity is O(min(n, m)), where m is the character‑set size, since that’s the maximum size our hash set can grow to.”

Visualizable Example: Annotated Solution with Verbal Script

Problem: Two Sum

function twoSum(nums: number[], target: number): number[] {
  const map = new Map();

  for (let i = 0; i < nums.length; i++) {
    const complement = target - nums[i];
    if (map.has(complement)) {
      return [map.get(complement)!, i];
    }
    map.set(nums[i], i);
  }

  return [];
}

Verbal script you could use

1. Restate – “Given an array nums and a target sum, we need to return the indices of the two numbers that add up to the target.”
2. Strategy – “I’ll use a hash map to store each number’s complement as we iterate, achieving O(n) time.”
3. Why it works – “If the current number’s complement has already been seen, the map will contain its index, guaranteeing we’ve found the pair.”
4. Example – Walk through nums = [2,7,11,15], target = 9. Show how after processing 2 we store 7 as its complement, then when we see 7 we find the match.
5. Complexity – “Time O(n) because we scan once; space O(n) for the map.”