Can an AI Really Think Like a Google Engineer?
We’ve all seen the headlines. AI is here to build our apps, write our code, and maybe even debug it at 3 AM while we get some much-needed sleep. But there’s a massive difference between spitting out code snippets and truly thinking like an engineer. An engineer analyzes, strategizes, considers edge cases, and weighs the trade-offs between different solutions. It’s not just about getting the right answer; it’s about getting the best answer for the situation.
So, can the latest generation of AI models, specifically Google's Gemini 2.5 Pro, step up and become a true AI coding assistant? Or is it just a glorified autocomplete? I decided to put it to the ultimate test: a classic Google coding interview question.
This isn't just about whether it can solve the problem. This is about seeing if it can demonstrate the kind of logical reasoning, foresight, and analytical depth that separates a junior coder from a senior developer. The results were... not what I expected.
The Challenge: A Classic Test of a Developer's Mindset
The problem I chose is a favorite in technical interviews because it looks simple on the surface but has layers of complexity. It's designed to see how a candidate approaches a problem, not just if they can write a loop. Watch the full test unfold in the video below.
Here’s the exact prompt I gave to my new AI coding assistant, Gemini 2.5 Pro:
Simple, right? Find the longest word. But a good engineer immediately thinks about the hidden challenges:
- What about punctuation? "Hello, world!" should probably consider "Hello" and "world".
- What if the string is empty?
- What if there are multiple spaces between words?
- What's the most efficient way to do this?
I wasn't just looking for code; I was looking for intelligence. I hit "Run" and waited to see what would happen.
Inside the Mind of Gemini: More Than Just Code Generation
This is where things got fascinating. Instead of just dumping a block of code, Gemini's "Thinking" panel revealed its entire strategy, step-by-step. This is a game-changer for anyone using Gemini 2.5 Pro for coding because it shows the why behind the what.
Here’s a summary of its thought process:
- Breaking Down the Request: It first identified the core task—finding the longest word—and the tie-breaker rule (return the first one).
- Formulating Word Processing Logic: It decided the first step was to split the string by spaces. Then, it realized it needed to clean the words by removing punctuation.
- Finalizing Edge Case Solutions: It actively considered what to do with empty strings, strings with only whitespace, and strings containing only punctuation. This is a critical engineering step that many junior developers (and many AIs) miss.
- Optimizing and Refining: It even went through multiple revisions of its regular expression for cleaning punctuation, aiming for a more robust and performant solution.
This wasn't just pattern matching. This was a structured, logical approach to problem-solving. It was thinking like an engineer.
The Verdict: Not One, But Three Solutions
After its internal deliberation, Gemini didn't give me one answer. It gave me three distinct solutions, each with its own style and trade-offs, complete with heavily commented code and examples.
1. The Classic `for` Loop (Most Readable)
This is the most straightforward method and the one most familiar to beginners. It's explicit, easy to debug, and perfectly handles the "first one wins" tie-breaker rule.
/**
* Finds the longest word in a string.
* @param {string} str The input string.
* @returns {string} The longest word in the string.
*/
function findLongestWord(str) {
// Step 1: Use a regular expression to remove punctuation and split the string into an array of words.
// The regex [^a-zA-Z0-9 ] matches any character that is NOT a letter, a number, or a space.
const words = str.replace(/[^a-zA-Z0-9 ]/g, "").split(' ');
// Step 2: Initialize a variable to hold the longest word.
// We'll start with an empty string.
let longestWord = "";
// Step 3: Loop through the array of words.
for (let i = 0; i < words.length; i++) {
const currentWord = words[i];
// Step 4: If the current word is longer than the longest word found so far,
// update the longestWord variable.
// The ">" comparison ensures that if two words have the same length, the first one is kept.
if (currentWord.length > longestWord.length) {
longestWord = currentWord;
}
}
// Step 5: Return the final longest word.
return longestWord;
}
// --- Examples ---
console.log(findLongestWord("The quick brown fox jumped over the lazy dog")); // "jumped"
console.log(findLongestWord("Google do a barrel roll")); // "Google"
console.log(findLongestWord("What is the average airspeed velocity of an unladen swallow")); // "airspeed"
console.log(findLongestWord("The words 'apple' and 'banana' are fruits.")); // "banana"
console.log(findLongestWord("Shortest long")); // "Shortest" (The first one is returned in a tie)
console.log(findLongestWord("")); // ""
2. Using `Array.prototype.sort()`
This method is more concise and elegant. It splits the string into words and then sorts the array by word length in descending order, making the first element the longest word.
/**
* Finds the longest word in a string using the sort() method.
* @param {string} str The input string.
* @returns {string} The longest word in the string.
*/
function findLongestWordSort(str) {
const words = str.replace(/[^a-zA-Z0-9 ]/g, "").split(' ');
// Sort the array by word length in descending order.
const sortedWords = words.sort((a, b) => b.length - a.length);
// The longest word is now the first element in the array.
// Return it, or an empty string if the input was empty.
return sortedWords[0] || "";
}
// --- Examples ---
console.log(findLongestWordSort("The quick brown fox jumped over the lazy dog")); // "jumped"
console.log(findLongestWordSort("Google do a barrel roll")); // "Google"
console.log(findLongestWordSort("What is the average airspeed velocity of an unladen swallow")); // "velocity"
// Note: The `sort` method is not "stable" in all JS engines, so for ties, the original order isn't guaranteed.
// The `for` loop or `reduce` method is better for strictly enforcing the "first one wins" rule.
3. Using `Array.prototype.reduce()` (Most Functional)
This is a powerful and very common pattern in modern JavaScript. The `reduce` method iterates over an array and reduces it to a single value—in this case, the longest word.
/**
* Finds the longest word in a string using the reduce() method.
* @param {string} str The input string.
* @returns {string} The longest word in the string.
*/
function findLongestWordReduce(str) {
const words = str.replace(/[^a-zA-Z0-9 ]/g, "").split(' ');
// The reduce function takes a callback and an initial value.
// 'longest' is the accumulator (the longest word found so far).
// 'current' is the current word being processed.
// The initial value for 'longest' is an empty string "".
return words.reduce((longest, current) => {
return current.length > longest.length ? current : longest;
}, "");
}
// --- Examples ---
console.log(findLongestWordReduce("The quick brown fox jumped over the lazy dog")); // "jumped"
console.log(findLongestWordReduce("Google do a barrel roll")); // "Google"
console.log(findLongestWordReduce("What is the average airspeed velocity of an unladen swallow")); // "airspeed"
console.log(findLongestWordReduce("The words 'apple' and 'banana' are fruits.")); // "banana"
console.log(findLongestWordReduce("Shortest long")); // "Shortest"
The Ultimate Comparison: Gemini's Own Analysis
Just providing the code wasn't enough. Gemini also generated a summary table comparing the different methods. This is exactly what a senior developer would do in a code review—explaining the trade-offs to help you choose the best tool for the job.
| Method | Pros | Cons | Best For |
|---|---|---|---|
| for Loop | Very easy to read and understand. Explicitly handles the tie-breaker rule perfectly. | More verbose than other methods. | Beginners or situations where maximum clarity is required. |
| sort() | Very concise and elegant. | Sorting is computationally more expensive (O(n log n)) than a single loop (O(n)). The "first one wins" rule isn't guaranteed in all JS engines for ties. | Quick, one-liner solutions where performance on huge strings isn't critical. |
| reduce() | Concise, declarative, and functional. Correctly handles the tie-breaker rule. | Can be slightly harder for beginners to understand. | A great balance of conciseness and correctness. It's a very common pattern in modern JavaScript codebases. |
Going Deeper: Can Gemini Analyze Its Own Code?
This is where the real Google interview challenge comes in. It's not enough to write working code; you need to understand its performance. This is measured using "Big O Notation," which describes how the time (Time Complexity) or memory usage (Space Complexity) of an algorithm grows as the input size grows. I decided to push my AI coding assistant further with this follow-up prompt:
The response was stunning. Gemini didn't just give me an answer; it gave me a masterclass in algorithmic analysis.
Gemini's Big O Breakdown: A Masterclass in Efficiency
Here is the complete, unabridged analysis provided by Gemini 2.5 Pro. It’s a detailed, step-by-step breakdown that defines its variables and explains the computational cost of each operation.
Defining Our Variables
To be precise, let's define our variables:
- n: The total number of characters in the input string,
str. - w: The number of words in the string.
It's important to note that w <= n. In Big O notation, we care about the dominant factor, which will be n.
1. The `for` Loop Solution Analysis
Here is the code again for reference:
function findLongestWord(str) {
const words = str.replace(/[^a-zA-Z0-9 ]/g, "").split(' ');
let longestWord = "";
for (let i = 0; i < words.length; i++) {
const currentWord = words[i];
if (currentWord.length > longestWord.length) {
longestWord = currentWord;
}
}
return longestWord;
}
Time Complexity: O(n)
Let's analyze the cost of each step:
str.replace(...).split(' '): This line does two things, both of which iterate through the string.replace()must scan the entire input string of length n to find and replace characters.split()must then scan the resulting string to find the spaces and create the words array. Combined, this step is O(n) + O(n) = O(n).let longestWord = "": This is a simple variable assignment. It's a constant time operation, O(1).- The
forloop: The loop runs once for every word in the words array. So, it runs w times. Inside the loop, all operations (.length, assignment) are O(1). Therefore, the total time for the loop is O(w).
Total Time Complexity: We add up the costs: O(n) (for replace/split) + O(1) + O(w) (for the loop). In Big O, we drop constants and non-dominant terms. Since w can never be greater than n, n is the dominant variable. Thus, the overall time complexity is O(n).
Space Complexity: O(n)
const words = ...: This is the most significant allocation. A new array is created to hold all the words from the string. In the worst-case scenario (e.g., a string like "a b c d"), the space required is proportional to the original string length n. This is an O(n) space cost.let longestWord = "": This variable stores a single word. In the worst case, it stores the longest word, which has a length of at most n. This is also an O(n) space cost.
Total Space Complexity: We add the space costs: O(n) (for the words array) + O(n) (for the longestWord variable). Again, we take the dominant term. The overall space complexity is O(n).
2. The `reduce` Solution Analysis
Here is the code again for reference:
function findLongestWordReduce(str) {
const words = str.replace(/[^a-zA-Z0-9 ]/g, "").split(' ');
return words.reduce((longest, current) => {
return current.length > longest.length ? current : longest;
}, "");
}
Time Complexity: O(n)
The analysis is almost identical to the `for` loop.
str.replace(...).split(' '): As before, this step involves scanning the string of length n twice. The complexity is O(n)..reduce(): The reduce method iterates through every element of the `words` array, just like the `for` loop. It runs the callback function w times. The callback itself performs constant time O(1) operations (comparison, returning a value). Therefore, the reduce operation has a time complexity of O(w).
Total Time Complexity: O(n) + O(w) = O(n).
Space Complexity: O(n)
const words = ...: Same as before. A new array is created, costing O(n) space.- The
reduceaccumulator (longest): The method's first argument to the callback holds the "result so far." This is analogous to thelongestWordvariable in the `for` loop. It requires space to hold the longest word found, which is at most n characters. This is an O(n) space cost.
Total Space Complexity: O(n) + O(n) = O(n).
This level of detailed, accurate analysis is precisely what an interviewer looks for. It demonstrates a deep understanding of not just how to write code, but how that code actually performs. I was genuinely impressed.
Final Verdict: Is Gemini 2.5 Pro Hired or Fired?
Hired. Absolutely hired.
What this experiment demonstrates is that we are entering a new era of AI coding assistants. Gemini 2.5 Pro proved it's not just a code generator; it's a collaborator. It's a tool that can not only provide multiple valid solutions but also teach you the underlying computer science principles and trade-offs behind them.
It approached the problem like an experienced engineer: clarifying requirements, considering edge cases, writing clean code, and analyzing its own work for performance. For junior developers, this is an incredible learning tool. For senior developers, it's a powerful sparring partner that can accelerate workflow and offer new perspectives.
What's Next? From Coding Challenges to Business Automation
Passing a coding test is one thing, but how does this translate to the real world? In our next series, we're leaving the theoretical tests behind. We're going to use Gemini 2.5 Pro as the brain of a fully automated system to manage a freelance business, connecting it with tools like Notion and Zapier.
If you want to see how we can build a system that automatically manages clients, projects, and invoices, make sure you're subscribed to the blog and our YouTube channel, @OpODab. The future of solo entrepreneurship is here, and it's powered by AI.
