LeetCode prep for AI-assisted coding interviews

Start with the task and tool rules from your recruiter. If the loop includes both algorithm questions and repository work, prepare for both. Permission to use AI in one round does not establish permission in another.

Attempt the problem yourself first. Write the input contract, a simple approach and a case where that approach becomes expensive. If you get stuck, ask for one question or a small hint, then return to your own reasoning. After you finish, explain the solution without the conversation open. Keep independent attempts separate from assisted ones.

The prompt below is for studying outside an interview. It asks the assistant to respond to your attempt instead of immediately supplying code. Assistants can ignore instructions or give incorrect hints, so check the advice against the problem's contract and tests.

I am studying an algorithm problem. Do not give the solution or code yet.
Problem and constraints: [paste only material you may share]
My approach: [describe it]
Case I cannot handle: [input, expected result, and my result]
Ask one question that helps me find the flaw. Wait for my attempt.
After I solve it, challenge my time and space complexity and one edge case.

A repository task adds discovery: find where the behavior lives, read its callers and tests, and understand which surrounding behavior must remain intact. The agent's ability to write code does not remove your need to identify the right requirement. A patch that sounds reasonable can still fix the wrong path or weaken validation.

For example, the public historical Django username-validation task on this site concerns a trailing newline. Before editing, write a check for an ordinary allowed username and another for the newline case. Find the actual validator and its contract before deciding how to reject the invalid input. If the agent proposes trimming input, ask whether that silently changes the requested behavior. This is a repository debugging exercise, not a LeetCode problem or a claim about current Django releases.

• Find the validator, callers and nearby tests before asking for a patch.
• Record whether the failing check reproduces the reported behavior on the supplied historical revision.
• Review the patch and run targeted and nearby regression checks. An environment error is not a passing test.
• Explain why the change satisfies the contract and which behavior you could not verify.

Download cache.py and test_cache.py above into the same empty folder. This original synthetic Python exercise turns a familiar data-structure idea into a small debugging task. It requires Python 3 and no packages, account or model purchase. It is not a question supplied by an employer or a SWE-bench task.

The cache contract is explicit: an entry expires when the clock reaches its expiration time; a zero-second TTL expires immediately; stored values including 0, False, an empty string and None remain valid until expiry. The supplied clock is nondecreasing, and TTL inputs are finite and nonnegative. Read the implementation and tests before asking an agent to edit.

The published starter produces four passing tests and six failing tests. The reference produces ten passing tests. We also checked three incomplete patches: fixing just the expiration comparison leaves five failures, fixing just the stored-value return leaves two failures, and fixing just the zero-TTL assignment leaves six failures because the boundary comparison is still wrong. These are reproducible checks for this example, not interview success statistics.

# In the folder containing cache.py and test_cache.py:
python -m unittest -v test_cache.py

# Suggested bounded request to your permitted assistant:
# Read the cache contract and the failing tests. Explain one violated
# requirement before editing. Keep all tests. Propose the smallest patch.
# After I review the diff, rerun the same command and explain each change.

Save the original failing output, your final diff and the rerun output. Explain which requirement each change repairs and which passing checks protected behavior that already worked. A green result after deleting assertions is not the same observation. Only then inspect the downloadable reference, which is one implementation of this exercise's contract.

For a follow-up, add a test that overwrites an existing key before expiry. State which expiration time should apply before asking the assistant to implement anything. This exercise does not evaluate concurrency, memory limits, cache eviction or security. Ten passing checks do not establish a production-ready cache or job readiness.

This suggested 90-minute session is for a loop that includes both formats. It is not an employer's time limit. If your interview covers only one format, spend the session on that format instead. Adjust the repository portion if environment setup consumes the time you set aside.

• First 25 minutes: attempt an unfamiliar algorithm problem without AI. Explain an example, a boundary case and the complexity.
• Next 10 minutes: review the attempt, using a hint only if needed. Record where you needed help and what you can now explain independently.
• Next 45 minutes: work on one bounded repository issue with the permitted agent. Find the relevant files, reproduce the failure, inspect the change and run the relevant checks.
• Last 10 minutes: explain the repository patch aloud and fill in the review record below. Choose one action to change in the next rehearsal.

Interview format and permitted tools:
Algorithm attempt: independent / hint-assisted / solution-reviewed
Reasoning or edge case I missed:
Repository requirement and affected files:
Reproduction command and observed result:
Agent suggestion I checked or rejected, and why:
Patch and regression commands with actual results:
Checks that could not run:
One action for the next rehearsal:

PraxisAI provides timed repository bug-fix rounds with an AI agent, alongside take-home project briefs. Use a challenge for the repository part of the plan. The public task brief and sample report let you inspect the workflow before choosing a round. For algorithm-only rehearsal, use a problem bank or your own exercises under the required tool rules.

Free practice includes a basic score, verdict and grading result. Paid plan access adds detailed process reports, prompt coaching and replay where evidence is available. Check the current allowance and model availability before starting. If the grading environment cannot run, the overall score is withheld. Recorded process scores describe that round and do not establish job readiness or an employer's verdict.