Sophie Novati

This course prepares software engineers for the **growing shift toward AI-assisted coding interviews**, where the rules of engagement have fundamentally changed. 

You'll learn how to strategically approach the first 10 minutes by gathering requirements and writing tests before touching any code, how to leverage AI for generating test cases while knowing when to rely on your own judgment for debugging, and how to communicate your thought process in a way that sounds controlled and intentional.

The course also covers how advanced data structures like tries, heaps, and LRU caches are re-entering the interview landscape now that AI handles their boilerplate implementation — and how to study for them by focusing on the "why" over the "how." To match what these interviews actually test, you'll also learn to shift your practice habits toward reading and tracing code rather than writing it from scratch. The core theme throughout is that AI changes the weight of each skill, not the skills themselves: typing speed matters less, while verification, clear communication, and strategic judgment matter more than ever.

How To Master AI-Assisted Coding Interviews

Introduction

Doing The Interview

Takeaways

What Should I Do In The First 10 Minutes?

What's Changing With AI-Assisted Coding Interviews?

Introducing The Active Users Problem

How Am I Being Evaluated Now?

When Should I Actually Use AI During The Interview?

How To Debug Effectively with AI As My Partner?

How Do I Talk Without Sounding Chaotic?

How Should I Practice For These Interviews?

What Are Advanced Concepts I Should Know?

In this lesson we talk about how to strategically approach the first 10 minutes of an AI-assisted coding interview to set yourself up for success.

- **Resist the urge to dive in immediately** — instead of jumping straight into coding or prompting the AI, start by restating the problem in your own words and doing a strategic skim of the codebase to understand existing functionality and function signatures.
- **Uncover hidden requirements** — as you explore the code, actively look for things like bot user filtering, non-active event handling, or data normalization that may not have been spelled out in the prompt, and clarify these with your interviewer.
- **Turn requirements into tests early** — unlike traditional interviews, convert each discovered requirement into an executable test case upfront (using AI to help generate them if needed), since verifying AI-generated code is a core part of the assessment.

In this lesson we talk about the rise of AI-assisted coding interviews and how they are changing the technical interview landscape.

- **Understand the new AI-assisted coding interview format:** Learn how modern interviews place you in a lightweight codebase (not a blank file) with existing helper functions, tests, and to-dos—plus an integrated AI chat panel that can access and assist with your code.
- **Navigate increased problem complexity:** Explore how today’s interviews include more substantial starter code, realistic product requirements (e.g., time zones), and sometimes more advanced algorithms and data structures—reflecting real engineering work.
- **Collaborate effectively with AI while staying in control:** Discover best practices for using the built-in AI strategically, communicating your thinking clearly, and maintaining ownership of the solution in a rapidly evolving interview landscape.

In this lesson we talk about a realistic example problem used throughout the course to simulate an AI-assisted coding interview.

- **Understand the problem context and deliverables:** Build the back-end logic for an active users metrics dashboard, including calculating daily unique active users and rolling 7-day active user counts from messy, real-world log data.
- **Work within a structured codebase:** Learn how to navigate starter code with helper functions (e.g., ID normalization, bot filtering) and focus on implementing two core functions while leveraging provided tests.
- **Set expectations for interview complexity:** Explore how AI-assisted interviews can range from simple LeetCode-style tasks to multi-file, production-like environments—and how this example reflects an average level of complexity.

In this lesson we talk about how candidates are evaluated in AI-assisted coding interviews and how the scoring rubric has shifted.

- **Understand what still matters—and what matters more:** Learn how the core engineering method (problem exploration, solution design, implementation, and testing) remains the foundation, but with greater emphasis on deep problem exploration and structured thinking before coding.
- **Recognize why problem solving is now the primary signal:** Because AI handles much of the typing, interviewers focus more heavily on your ability to form a clear plan, choose the right algorithms and data structures, and articulate trade-offs.
- **Master delegation and verification with AI:** Discover how execution now means directing AI effectively—accelerating typing but not thinking—and how careful verification of AI-generated code is a major part of your evaluation.

In this lesson we talk about when and how to use AI effectively during an AI-assisted coding interview.

- **Find the right balance with AI:** Learn how to avoid over-delegating core logic to AI or underusing it and running out of time, by aiming for the “middle path” that maximizes leverage while minimizing risk.
- **Accelerate typing, not thinking:** Discover why you should use AI for scaffolding, boilerplate, and clearly defined test cases—but retain ownership over algorithms, data structures, and key design decisions.
- **Optimize for easy verification:** Understand that AI is most helpful when you already know what needs to be built and can quickly verify the output, and least helpful when you’re still figuring out the correct approach.

In this lesson we talk about how to debug effectively with AI as a partner during a coding interview, and when to use it versus when to rely on your own judgment.

- **Use AI to generate more test cases, but step through code yourself** — AI can rapidly produce test cases that would take much longer to write manually, but since it predicts words rather than executing code, it can reproduce the same bugs it originally introduced, making it unreliable for tracing logic line by line.
- **Avoid leaning on AI for debugging, but use it strategically when stuck** — rather than asking AI to find or fix a bug directly, prompt it to brainstorm multiple possible causes, which keeps you in the driver's seat, surfaces hypotheses for you to validate with your own judgment, and reduces the risk of confidently wrong AI suggestions.
- **Always rerun all tests after a fix, not just the failing one** — thorough test coverage lets passing tests serve as reliable verification; if your test suite is thin, compensate with more manual code inspection to avoid introducing regressions you won't have time to track down later.

In this lesson we talk about how to communicate effectively during an AI-assisted coding interview without sounding scattered, frantic, or going completely silent.

- **Avoid the two extremes** — narrating every single keystroke comes across as junior, while going silent for several minutes leaves the interviewer in the dark; the goal is to sound controlled and intentional somewhere in between.
- **Choose the communication model that fits your thinking style** — Model A is light, continuous commentary as you work ("I'm pulling the date out of each event to populate the per-day set"), while Model B is stating your high-level intent upfront, working silently for 60–90 seconds, then summarizing what you did before repeating the cycle.
- **Practice talking while you work before the interview** — like touch typing, verbalizing your thought process while solving a problem is a skill that needs to be rehearsed, not figured out on the spot; a few mock sessions with a friend or mentor can make a significant difference.

In this lesson we talk about how AI is making previously time-prohibitive advanced data structures and algorithms newly relevant in coding interviews, and what that means for how you should prepare.

- **AI unlocks harder concepts by handling the boilerplate** — complex structures like tries, heaps, and LRU caches were rarely testable in a 45–60 minute interview because implementation alone could eat up 15–20 minutes; with AI generating that scaffolding in seconds, interviewers can now reasonably include them, making them worth studying once you've solidified the fundamentals.
- **What you actually need to demonstrate has shifted** — since AI handles much of the implementation, your value lies in recognizing when a given data structure is the right tool, clearly articulating *why* it helps (e.g. "a trie eliminates the factor of M by checking all patterns simultaneously through shared prefix traversal"), and writing precise prompts that reflect that understanding.
- **Master the basics first, and don't over-rotate on this** — the vast majority of interviews (roughly 95%) will not require these advanced concepts, so treat this as the final 5–10% polish layer, not the foundation; verification of AI output remains just as critical here as everywhere else in the interview.

In this lesson we talk about how to effectively prepare for AI-assisted coding interviews, with a focus on shifting your study habits to match what these interviews actually test.

- **Learn the "why" before the "how"** — rather than drilling implementations, focus on understanding the canonical problem each data structure solves and practice explaining it in one sentence out loud; with AI handling the code, being unable to independently identify the right approach is now much harder to hide or compensate for.
- **Know the common gotchas for each pattern** — every classic structure has a specific edge case trap (e.g. the `is_end` flag in a trie, recency updates in an LRU cache, off-by-one errors in sliding windows); getting familiar with these upfront dramatically speeds up your ability to verify AI-generated output during the interview.
- **Practice reading code, not just writing it** — since interviews now involve more time spent reading starter code and verifying AI output than typing from scratch, deliberately train yourself to mentally trace logic quickly by reading existing solutions online or having AI generate code for problems you already understand, then tracing through them by hand.