How a Former Meta Engineer Tackles an AI Coding Interview in Real Time

In this walkthrough, a former engineer from Meta and current co-founder of Hello Interview demonstrates exactly how to approach one of these modern AI coding interviews in real time.

Instead of presenting a polished solution, the session intentionally shows the messy reality of problem-solving: uncertainty, debugging mistakes, complexity analysis, optimization decisions, and intelligent AI usage.

What Makes AI Coding Interviews Different?

Traditional coding interviews focus heavily on raw implementation ability. AI coding interviews introduce a new dimension: how effectively a candidate can use AI as a productivity tool while still demonstrating strong engineering judgment.

The key mistake many candidates make is avoiding AI entirely.

According to the interview coach in this walkthrough, interviewers actually expect candidates to use AI. The evaluation is no longer “Can you code everything yourself?” but rather:

Can you guide the AI effectively?
Can you validate its output?
Can you debug incorrect suggestions?
Can you think critically instead of blindly accepting generated code?

In other words, engineers are being evaluated on AI collaboration skills just as much as coding skills.

The Problem: Finding “Container” Words

The coding challenge revolves around a list of words.

A word is considered a container if another word in the dataset appears inside it as a substring.

Example

"apple" contains "app"
"banana" contains "ban"
"application" contains "app"

So the algorithm should return:

apple
banana
application

while ignoring standalone words that contain nothing else.

At first glance, the problem looks simple. But as the walkthrough shows, performance constraints eventually force multiple optimizations and expose the dangers of trusting AI too quickly.

Phase 1: Understanding the Codebase

The first lesson is surprisingly important:

Don’t rush into coding immediately.

The engineer starts by exploring:

the main() function,
helper classes,
validation logic,
and existing test cases.

Instead of manually reading every line, AI is used strategically to:

summarize classes,
generate concise comments,
and explain function responsibilities.

This is an excellent example of productive AI usage. Rather than asking AI to “solve the problem,” the engineer uses it to accelerate comprehension.

That distinction matters.

Using Tests to Discover Bugs

Before implementing the solution, several hidden bugs are intentionally planted in the codebase.

One important bug involved substring validation logic.

The original condition incorrectly allowed a word to contain itself:


if len(contained) <= len(container):

The fix was changing it to:


if len(contained) < len(container):

This small detail demonstrates an important interview behavior:

understanding assumptions,
validating edge cases,
and explaining reasoning clearly.

Another bug involved empty strings being treated as valid words.

Again, the engineer didn’t just fix the issue — he explained why the behavior was incorrect.

That communication is often what separates strong candidates from average ones.

Building the First Solution

The initial implementation used a straightforward brute-force strategy.

Brute Force Idea

For every word:

compare it against every other word,
check whether one is a substring of the other,
and collect matches.

Conceptually:


for each word:
    for every other word:
        check substring

The AI assistant was then instructed to implement this exact strategy.

Notice the difference:

The human defined the algorithm.
The AI handled syntax and boilerplate.

That’s ideal AI collaboration.

Complexity Analysis Matters

The engineer carefully analyzed the runtime:

$O(n^2 \cdot m)$

Where:

n = number of words
m = average word length

The nested loops produced the n² factor, while substring checking added the m.

Initially, the solution passed all correctness tests.

But large datasets eventually caused performance failures.

First Optimization: Hash Set Lookup

To improve performance, the engineer introduced a smarter idea.

Instead of comparing every word against every other word:

Store all words inside a hash set.
Generate substrings for each word.
Check substring existence in constant time.

This reduced lookup cost dramatically.

The expected complexity became:

$O(n \cdot m^2)$

At first, the optimization looked successful.

Then the AI-generated code failed.

The Most Important Lesson: AI Is Often Wrong

The AI accidentally counted a word as containing itself.

The bug slipped into generated code because the AI checked:


if substring in word_set:

without excluding:


substring != word

The engineer caught the issue by:

adding print statements,
tracing outputs,
and manually reasoning through the logic.

This moment is extremely important.

It demonstrates the exact skill interviewers are looking for:

skepticism,
debugging ability,
and ownership over AI-generated code.

The AI accelerated implementation — but human judgment was still essential.

Discovering a Hidden Complexity Problem

Even after optimization, another issue emerged.

The engineer realized substring slicing itself was expensive.

The actual complexity wasn’t:

$O(n \cdot m^2)$

but rather:

$O(n \cdot m^3)$

because creating substrings in Python allocates new memory.

This insight only appeared through careful reasoning — something AI initially missed.

Again, this highlights a recurring theme:
AI can help implement solutions quickly, but deep algorithmic understanding still matters.

Final Optimization: Trie-Based Search

To handle extremely long words efficiently, the engineer eventually moved to a Trie-based solution.

A Trie stores words as a character tree structure.

Instead of repeatedly generating substrings, the algorithm:

inserts all words into a Trie,
traverses possible matches character-by-character,
and avoids expensive substring allocations.

This dramatically improved performance on large datasets.

The optimized solution finally passed every performance test.

What Interviewers Actually Want to See

Throughout the session, several patterns consistently stood out.

Strong Candidate Behaviors

Explaining tradeoffs clearly
Thinking aloud
Verifying assumptions
Using tests intelligently
Leveraging AI without depending on it
Debugging generated code carefully
Analyzing complexity accurately

Weak Candidate Behaviors

Asking AI for the full solution immediately
Blindly trusting generated code
Failing to explain reasoning
Ignoring complexity analysis
Panicking during debugging

Modern interviews increasingly reward engineering judgment over memorized solutions.

The Real Skill Being Evaluated

The biggest takeaway from this walkthrough is simple:

AI coding interviews are not testing whether you can out-code AI.

They are testing whether you can:

think critically,
guide AI effectively,
and produce reliable engineering outcomes under pressure.

The best candidates are not the ones who avoid AI.

They are the ones who know exactly:

when to use it,
how to use it,
and when not to trust it.

As AI becomes integrated into real engineering workflows, those skills are quickly becoming essential in technical interviews — and in software development itself.

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