Use AI as an interviewer and reviewer, not as the person solving the problem: make an independent attempt first, then ask for targeted feedback without requesting code. This keeps your practice focused on the skills a coding interview asks you to demonstrate—clarifying a problem, reasoning aloud, writing and testing code, and explaining why your approach works.
Use this five-step practice routine
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Set the conditions and clarify the problem
Choose a problem and set a timer. Before coding, restate the task in your own words, identify the expected inputs and outputs, note constraints, and work through an example. If something is ambiguous, write down the question you would ask an interviewer.
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Plan aloud before you implement
Describe a possible approach and its trade-offs before opening an editor. If you want an AI interviewer, ask it to present a prompt or ask follow-up questions, but tell it not to provide code or a solution. For example: “Act as an interviewer. Give me one coding problem, answer clarifying questions, and ask follow-ups. Do not give hints or a solution unless I explicitly ask after my attempt.”
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Write and test your own solution
Implement your approach without generated code. Test the supplied example and cases that could expose boundary errors, such as empty input, a single item, duplicates, or values at the constraint limits when those cases apply. Explain what each test is checking.
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Explain correctness and complexity
Once the code works on your tests, explain why the approach produces the required result. State its time and space complexity and connect those costs to the operations your implementation performs. Treat an AI-generated assessment as a prompt for review, not proof that your reasoning is correct.
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Ask for one useful critique
Share your attempt and ask AI to identify one possible missed case, ask a follow-up, or critique your explanation without revealing a complete solution. If you later request a full explanation, use it only after your independent attempt: close it, then reconstruct the approach from memory and explain why it works.
Keep a short error log after each session: misunderstanding the prompt, missing an edge case, choosing an unsuitable data structure, making an implementation error, or explaining the reasoning poorly. Use the log to choose what to practice next.
Choose a practice format that preserves independent effort
A useful mock interview should exercise more than code generation. Compare options by whether they require an unaided first attempt, reveal hints gradually, support verbal reasoning and follow-ups, let you run code against tests, and give feedback on both reasoning and communication. The most important fit is the real interview’s format and rules.
For a concrete example, HackerRank’s Coding Mock Interview documentation describes a timed, 60-minute format in which an interviewer presents a role-specific coding task, permits clarifying questions, asks follow-ups, allows code execution and test review, and provides a feedback report. Documented feedback dimensions include code quality, problem-solving, technical communication, and language proficiency. The help page says a microphone is needed only for speech input and recommends an uninterrupted hour; availability and credit requirements can change. This describes that service, not a comparative finding that it is better than other practice methods.
Keep AI feedback in perspective
A small 2025 exploratory study by Daryanto and colleagues reported that 17 participants valued conversational AI for simulation, feedback, and learning from generated examples during think-aloud technical interview practice. It suggests ways such tools may support preparation, but does not establish improved hiring or interview outcomes at scale. Read the study abstract.
A separate 2023 evaluation by Ouh and colleagues examined 80 undergraduate Java programming exercises. The authors reported that ChatGPT-generated solutions could be readable and well organized, while exercises with non-textual descriptions or class files could lead to invalid solutions. This was an introductory Java course evaluation, not a study of coding-interview learning. It is a reason to check generated code independently rather than treat a polished answer as correct. Read the paper record.
These findings do not establish that a particular number of practice problems—or this specific routine—will raise a candidate’s pass rate. Use AI to create structured practice and expose questions to examine, while judging progress by what you can solve and explain without it.
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Do not assume that a practice tool’s rules carry over to an employer’s interview. Policies vary by company and interview format. Anthropic’s candidate guidance says its live interviews are AI-free unless otherwise indicated; the guidance was last updated July 10, 2025. See Anthropic’s hiring-process guidance. OpenAI likewise says expectations vary by interview, with some formats allowing tools and others assessing independent problem-solving; it directs candidates to the relevant preparation materials or recruiter. See OpenAI’s Interview Guide. These are company-specific examples, not universal rules. Follow the instructions for your own interview.
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