Longest Common Prefix — Explained Simply for Interviews

Learn how to solve the Longest Common Prefix problem the way interviewers expect — column-by-column thinking, clean code, and zero panic.

What the Interviewer Is Really Testing#

When an interviewer hands you the Longest Common Prefix problem, they're not just checking if you know the answer. They've seen hundreds of candidates solve this. What they're actually watching for is how you think.

Specifically, they want to know:

• Can you break a string problem into something visual and logical?
• Do you handle edge cases before you write code, or do you get burned by them mid-solution?
• Can you recognize when a simple approach is the right approach — and resist the urge to overcomplicate it?

This is a classic early-round or warm-up problem. Nail it clean, and you set a confident tone for everything that follows.

The Problem, In Plain English#

You're given an array of strings. Find the longest prefix that all strings share.

["flower", "flow", "flight"] → "fl"
["dog", "racecar", "car"]   → ""

Simple enough, right? Here's the thing most people miss — the mental model matters more than the code. If you think about this wrong, your code will be messy, buggy, and hard to explain out loud.

The Mental Model That Changes Everything#

Most candidates instinctively start comparing strings to each other — like, "Is string 1 a prefix of string 2? Is string 2 a prefix of string 3?" That approach works, but it leads to awkward logic and easy bugs.

Here's the better way to think about it: think in columns, not rows.

Imagine stacking your strings on top of each other and reading down each column:

f  l  o  w  e  r
f  l  o  w
f  l  i  g  h  t
✅ ✅ ❌ stop

You go column by column. As long as every string has the same character in that column, you keep going. The moment one string differs — or runs out of characters — you stop and return what you've accumulated so far.

This is the "column-by-column" approach, and once you see it this way, the code writes itself.

The Best Approach: Character-by-Character#

Here's the clean solution. Walk through the first string's characters. For each position i, check if every other string has that same character at position i. If anything breaks — stop.

class Solution {
    public String longestCommonPrefix(String[] strs) {
        if (strs == null || strs.length == 0) return "";
 
        for (int i = 0; i < strs[0].length(); i++) {
            char c = strs[0].charAt(i);
 
            for (int j = 1; j < strs.length; j++) {
                if (i >= strs[j].length() || strs[j].charAt

Let's break down what's happening:

1. Null/empty check first — always. This is a signal to the interviewer that you think defensively.
2. We use strs[0] as our reference string — we only need to iterate as far as the shortest common match, and the outer loop naturally stops when the first string runs out.
3. The i >= strs[j].length() check is critical. If a shorter string is exhausted, it can't share a longer prefix. Stop immediately.
4. If everything matches through the entire first string, return strs[0] — it's the prefix itself.

Complexity Breakdown#

When the interviewer asks "what's the time complexity?", walk them through it like this: "In the worst case, I'm comparing every character of every string. If there are n strings and the shortest one has m characters, that's O(n × m) total comparisons." That's the answer they want.

Where Candidates Get Stuck#

I've watched a lot of people fumble this problem — not because it's hard, but because of very specific, avoidable mistakes. Here's what to watch out for:

• Forgetting to check string length before accessing a character. This causes an IndexOutOfBoundsException and immediately signals to the interviewer that you're not thinking about edge cases. Always check i >= strs[j].length() before strs[j].charAt(i).

• Comparing full strings instead of characters. Some candidates try strs[j].startsWith(strs[0]) inside a loop. This might work in some forms, but it's the wrong mental model and breaks down when you're asked to explain it.

• Forgetting the empty array edge case. If the input is [] or null, your code should return "" immediately. Not handling this is a red flag — it tells the interviewer you don't think about inputs before you start coding.

• Overcomplicating with sorting. A common trap: "I'll sort the array, then compare the first and last string." This actually works (sorted strings maximize difference), but it's O(n log n) and harder to explain cleanly. Unless the interviewer asks for it as a follow-up, keep it simple.

• Panicking when the answer is an empty string. Some candidates second-guess themselves when the result is "". That's a valid answer! Own it.

The Alternative: Prefix Shrinking#

There's another approach worth knowing — especially because interviewers sometimes ask "can you think of another way?"

Instead of going character by character, start with the entire first string as your candidate prefix. Then go through each subsequent string and shrink the prefix until it fits.

public String longestCommonPrefix(String[] strs) {
    if (strs == null || strs.length == 0) return "";
 
    String prefix = strs[0];
 
    for (int i = 1; i < strs.length; i++) {
        while (!strs[i].startsWith(prefix)) {
            prefix = prefix.substring(0, prefix.length() - 1);
            if (prefix

This is elegant and easy to explain out loud. The trade-off? Each call to substring creates a new string object, so this is slightly worse in practice for large inputs. But the logic is intuitive: "Start with the biggest possible prefix and shrink it until it works."

Comparing the Two Approaches#

My honest advice: code the character-by-character solution, but be ready to describe the prefix-shrinking approach if the interviewer asks for alternatives. It shows you can think about the same problem multiple ways.

How to Talk Through This in an Interview#

This is where most candidates lose points — not in the code, but in the communication. Here's exactly how I'd recommend narrating your thought process:

Step 1 — Clarify before you code:

"Before I jump in — just to confirm, we're looking for a prefix common to all strings, right? Not just a common substring? And I should return an empty string if there's no common prefix?"

This takes 10 seconds and shows you read carefully.

Step 2 — State your approach out loud:

"My initial approach would be to think of this column by column. I'll iterate through the characters of the first string, and for each position, check if every other string has the same character there. The moment something doesn't match, I return what I have so far."

Step 3 — Call out edge cases before coding:

"Edge cases I'm thinking about: empty array, single string in the array, strings of different lengths — especially when a shorter string is a prefix of a longer one, like 'fl' in an array with 'flower'."

Step 4 — Code it up, narrating as you go:

"I'll start with a null and empty check here... Now I'm using the first string as my reference... This check — i >= strs[j].length() — is important because a shorter string could run out before we find a mismatch on character values..."

Step 5 — Verify with the examples:

"Let me trace through ['flower', 'flow', 'flight'] quickly... position 0: f, f, f — match. Position 1: l, l, l — match. Position 2: o, o, i — mismatch, so we return strs[0].substring(0, 2) which is 'fl'. That matches the expected output."

This is the kind of structured, confident walkthrough that makes interviewers write "strong hire" in their notes.

Follow-Up Questions — And How to Handle Them#

Expect the interviewer to push you after you solve it. Here's what they'll typically ask and how to respond:

"What if the array has only one string?" Easy — your loop never runs the inner comparison, and you return strs[0]. Walk them through it in the code.

"Can you think of a different approach?" This is your chance to describe the prefix-shrinking method. Bonus points if you mention the sorting trick — sort the array, then compare just the first and last string, since those will be the most different lexicographically.

"How would this scale if you had millions of strings?" Here's where you get to show system thinking: "The character-by-character approach is already O(n × m), which is optimal — you have to look at every character at least once. If memory is a concern, this approach is great because it's O(1) space. For truly massive datasets, you could parallelize the column comparisons, but for a single machine, this solution holds up well."

"What if strings can contain Unicode characters, not just ASCII?" Great question to anticipate. In Java, charAt() can have issues with certain Unicode characters (surrogate pairs). You could mention using codePointAt() for a more robust solution. This signals real-world awareness.

Red Flags to Avoid#

These are the things that make interviewers lose confidence in you — avoid them:

• Jumping straight to code without clarifying — even 30 seconds of verbal setup shows maturity
• Not handling the empty array case — this is the most common way candidates get tripped up
• Silent coding — narrate your thinking; interviewers can't read your mind
• Saying "I think this works" without tracing through an example — verify it yourself, out loud
• Getting defensive if the interviewer points out a bug — say "great catch, let me fix that" and keep moving
• Using a more complex algorithm when a simple one works — if O(n × m) solves it cleanly, don't bring in a trie just to look smart

Key Takeaways#

Here's what to lock in before your interview:

• Think in columns, not rows. Visualize characters stacked vertically and scan down each column.
• Always handle null/empty input first. It's the first thing interviewers notice.
• Check string length before accessing a character — i >= strs[j].length() before charAt(i). Always.
• The character-by-character approach is O(n × m) time, O(1) space — know how to explain this clearly.
• Know the prefix-shrinking alternative — it's a great answer to "can you think of another way?"
• Talk as you code. Your communication is being evaluated as much as your solution.
• Trace through the examples yourself before declaring your solution correct.

This problem seems simple, and it is — but simple problems have a way of exposing bad habits. Nail the communication, handle the edge cases, and explain your complexity clearly. That's what separates a good answer from a great one.