Here’s something I find genuinely useful to say about live coding interviews: the hardest part usually isn’t the algorithm. It’s thinking out loud in front of someone while a timer is running and a recruiter you’ve never met is watching you type. The cognitive load of the social situation degrades the technical performance, and nobody really talks about how to address that specifically.
AI tools have changed the prep landscape here. But a lot of advice on “using AI for coding interviews” is vague or glosses over the actual line between preparation and cheating. I want to be concrete about both.
What “AI support” actually means (and what it doesn’t)
Using AI during a live interview where you’re not supposed to is cheating. That line is clear. Most companies explicitly prohibit outside resources during coding assessments, and using an AI to generate solutions while the interview is running violates the point of the exercise, even if the platform can’t detect it. I’d recommend against it, not just for ethical reasons but practical ones: if you land a role by cheating and then can’t do the work, that’s a bad situation for everyone.
What AI is genuinely useful for is the practice and the preparation. That’s where the real value is anyway. The Stack Overflow Developer Survey 2024 found that 76% of developers now use AI tools in their development workflows. The ones who use them well tend to use them to learn faster, not to skip learning.
Using AI to practice thinking out loud
This is the most underrated use case. The skill of explaining your reasoning while you code is distinct from the skill of writing correct code, and it atrophies if you only practice silently on LeetCode.
A good AI interview practice session looks like this:
- Tell the AI to give you a medium-difficulty problem and act as a silent interviewer
- Narrate every step out loud (or typed), including wrong turns
- When you get stuck, ask for a hint framed as a question (“what pattern would help here?”) rather than asking for the solution
- After you finish, ask the AI to evaluate your communication, not just your code
That last step is the one most people skip. An AI that evaluates whether your explanation was followable from a non-expert perspective is practicing a different muscle than one that just tells you whether your code is correct.
Craqly’s interview practice mode is designed around this loop: you get a problem, you work through it out loud, and the AI gives feedback on both your approach and your communication. I’ve found the communication feedback more useful than the algorithmic feedback in most practice sessions, which says something about where people tend to be undercoached.
Pattern recognition as a practice accelerator
One concrete thing AI can help with: when you’re stuck on a problem category, ask “what pattern does this problem type typically use?” rather than “solve this problem.” This builds the recognition reflex that matters in real interviews.
The most common patterns worth drilling with this approach are two pointers, sliding window, BFS/DFS, heap operations, and dynamic programming. If you can describe in one sentence why each of those applies to the right problem type, you’re in decent shape for most mid-level coding rounds. (For senior and staff-level roles, the problems often combine two patterns, which is where the practice of narrating your thought process pays off most.)
The first three minutes of a live coding interview
Most candidates rush. They hear the problem, nod, and start typing. This is almost always a mistake, even when the problem feels familiar. Here’s a more reliable approach:
- Repeat the problem in your own words before you touch the keyboard
- Ask one constraint question (input size, edge cases on empty input, whether the array is sorted)
- Work through one example on paper or in comments, not in code
- State your approach before you implement it (“I’m going to use a sliding window here because…”)
Interviewers at companies like Google, Meta, and Stripe consistently report that candidates who do this well look significantly more senior than their actual level. It signals structured thinking, which is the real thing being evaluated. The LinkedIn Economic Graph’s 2024 skills analysis ranks “complex problem solving” and “communication” as the top two technical hiring criteria, and this first-three-minutes approach addresses both simultaneously.
Getting unstuck without embarrassment
People freeze. It happens in practice sessions and it happens in real interviews. The two most effective ways I’ve seen people get unstuck are: (1) say what you know, not what you don’t (“I can see this is a graph problem, I’m just working out whether it’s BFS or DFS”) and (2) ask the interviewer for a constraint clarification you don’t actually need yet.
The second one sounds strange but it works. Saying “just to confirm, the values can be negative?” gives you 15 seconds to think and signals to the interviewer that you’re thinking about edge cases rather than panicking. Most interviewers respond warmly to this.
In AI practice sessions, you can train this specifically: have the AI play an interviewer who won’t give hints unless you ask a specific, well-formed question. This makes the recovery pattern feel automatic by the time you’re in a real interview.
After the interview: the part nobody does
Write down the problem you got, your approach, and where you got stuck. Do this within an hour of finishing. I don’t have data on how many people actually do this, but my guess is it’s under 10%, which is a shame because it returns more improvement per hour than almost any other practice habit.
Then go solve the problem again, correctly, the next day. Not to punish yourself. To close the loop. The problem you stumbled on in an interview is more memorable than a random practice problem, which makes it better study material.
Does AI make live coding interviews easier to pass? For prepared candidates, probably yes. For unprepared ones, it mostly just shifts the anxiety around. The preparation is still the work.