Here is something I have noticed about technical interview prep: most of the advice online is about what to practice, not how to practice effectively. Grind LeetCode, do system design flashcards, memorize the Blind 75. Fine. But if your practice sessions feel like going through motions, you are probably not building the thing that actually matters in the room: the ability to explain your thinking to another person under pressure.
AI tools (the good ones, used right) help with this specifically. Not by solving the problems for you, but by creating a feedback loop that a static problem set cannot give you.
This is what has actually worked for me and people I have watched prep for jobs at Stripe, Databricks, and a few smaller companies.
What makes technical interviews different from other interview formats
The obvious thing is that you have to write code or draw architecture diagrams live. But the real challenge is that interviewers are watching two things simultaneously: whether you produce a correct solution, and whether you can think out loud in a way they can follow.
A lot of candidates can solve the problem. Fewer can narrate their way through it. The narration is actually what most interviewers are evaluating, especially at mid-to-senior levels where the assumption is that you can figure out the algorithm given time.
Worth noting: this varies. At some companies (and I do not have hard data on which ones specifically) the coding screen is almost entirely an accuracy filter and the communication part matters less. At others, a candidate who talks through an imperfect approach clearly will beat a candidate who produces correct code in silence. You have to read the room or ask your recruiter directly what the interviewers weight most.
Coding rounds: how AI feedback actually helps
The standard LeetCode grind gives you a verdict (pass/fail) and an editorial. What it does not give you is someone saying “you described the tree traversal correctly but then you jumped to the solution without explaining why you chose DFS over BFS.”
That gap is where AI assistance is useful. Here is a workflow that works:
- Pick a problem. Work it out loud, talking through your approach as if the interviewer is there.
- Paste your verbal explanation (just type it up roughly) along with your code into an AI tool and ask: “Does my explanation match my implementation? Are there points where I would lose an interviewer?”
- Iterate on the explanation, not just the code.
This sounds obvious but almost nobody does it. Most people practice the code and assume the narration will come naturally. It does not. It is a different skill and it gets better with repetition exactly like the algorithm work does.
One specific thing: ask the AI to play interviewer and push back on your approach. “Why not do this iteratively instead of recursively?” If you cannot answer that in real time, you will get stuck when the interviewer asks it live. (This happens more than you would think. Even at companies with well-run interviews.)
System design: the format where AI prep works best
System design is the format where AI assistance gives the clearest return. Here is why: unlike coding, there is no single right answer to “design a rate limiter for an API with 50 million requests per day.” There are tradeoffs, and the job is to demonstrate that you understand them.
AI tools are genuinely good at stress-testing your design choices. A session I have found useful:
- State your approach to a system design problem out loud or in writing.
- Ask the AI: “What are the weakest parts of this design? What would break at 10x scale? What did I not address?”
- Work through the gaps.
- Repeat for the same problem from a different angle (e.g., now optimize for latency instead of throughput).
You are not trying to memorize correct designs. You are trying to build the habit of thinking about failure modes and tradeoffs, which is what senior engineers actually do in real systems work. The Stack Overflow 2024 Developer Survey found that systems thinking and architecture ranked as the top skills developers felt they needed to develop further, above language-specific skills. That maps pretty well to what system design interviews are actually testing.
Technical deep-dive interviews
This is the format where a lot of people feel most comfortable (it is about your own experience!) and where they actually make the most mistakes.
The mistake is telling the story of a project instead of the story of a decision. “We built a service using Kafka and Redis” is not interesting. “We tried Kafka first, it introduced too much latency for our use case, and we switched to a simpler queue and accepted the tradeoffs” is interesting.
AI prep here looks like this:
- Write up 2-3 of your most technically interesting projects, focusing on decisions made and tradeoffs accepted.
- Ask an AI to ask you follow-up questions as a skeptical senior engineer. “Why Redis and not Memcached? Did you benchmark?” and so on.
- Keep answering until you either have a solid response or find that you actually do not know the answer, in which case you now know what to look up before the interview.
What AI cannot do for you
Worth being direct about this because there is a lot of hype around AI interview tools. AI cannot replace the repetitions. It cannot make you faster at writing real code under time pressure. It cannot simulate the specific social dynamic of a real interview, where there is another person across a table (or a Zoom call) watching you and you can feel whether they are engaged or losing interest.
The Zoom waiting room music does not exist in a mock session. The nerves are real in a real interview in a way that no simulation fully replicates. You need both: AI-assisted prep for depth and feedback, and real human mock interviews (friends, peers, paid services) for the actual simulation.
Tools like Craqly’s interview copilot sit in a useful middle ground: they give you real-time feedback during practice sessions so you can catch the gaps without needing to schedule time with a human every session. That is the right way to think about them, as a feedback accelerator, not a replacement for doing the work.
A few things that are probably overrated
I think the Blind 75 problem list is overrated for senior interviews. There, I said it. It is a useful starting point for people newer to LeetCode-style prep, but if you have been coding for several years, spending your last two weeks grinding easy/medium problems you already know is not the best use of time. You are better off doing 12 hard problems you have never seen with the narration-practice workflow above than 80 problems in silence.
The other thing I would push back on: flashcard-style system design prep. Memorizing the components of a Twitter clone does not help as much as doing 5 live design sessions where you articulate tradeoffs out loud. The interview is not a knowledge test. It is a thinking-out-loud test.
Will this advice be wrong for some people? Probably. There are roles and companies where grinding volume is exactly what you need. Check with someone who has interviewed at the specific company recently. That information is worth more than general frameworks.