What to Expect
The code interview is a hands-on assessment where you’ll write actual code while discussing your approach with the AI interviewer.- Duration: ~60 minutes (can run up to 90 minutes)
- Format: One or more coding problems with real-time conversation
- Focus areas: Code quality, problem-solving, algorithm design, and communication
- Environment: Browser-based code editor with support for multiple languages
What’s Being Evaluated
| Area | What the AI Looks For |
|---|---|
| Correctness | Does your code produce the right output? |
| Problem-solving | Can you break down the problem and identify an approach? |
| Code quality | Is your code clean, readable, and well-structured? |
| Communication | Can you explain your approach and decisions as you code? |
| Edge cases | Do you consider boundary conditions and error handling? |
| Optimization | Can you analyze and improve time/space complexity? |
The AI evaluates your full approach — understanding the problem, planning a solution, coding it, and testing it. A working solution with clear reasoning scores better than a “perfect” solution you can’t explain.
How to Prepare
Before You Start
- Know your language. Make sure you’re comfortable with the syntax and standard library of whichever language you plan to use.
- Practice problem-solving out loud. The biggest difference from LeetCode-style prep is that you need to narrate as you go. Practice explaining your thought process while coding.
- Review common patterns. Depending on the role level, brush up on arrays, strings, hash maps, trees, graphs, dynamic programming, or system design — whatever’s relevant.
- Prepare your environment. Test your camera, mic, and internet. Make sure your browser supports the coding interface. See Technical Requirements.
During the Interview
- Start with understanding. Read the problem carefully. Restate it in your own words. Ask clarifying questions before writing any code.
- Plan before you code. Outline your approach verbally or in comments. Discuss time/space complexity upfront.
- Talk as you code. Explain what you’re writing and why. “I’m using a hash map here because…” is exactly the kind of narration that helps.
- Test your solution. Walk through your code with example inputs. Check edge cases. Don’t wait for the AI to find bugs.
- Iterate. If your first approach works but isn’t optimal, discuss potential improvements even if you don’t have time to implement them.
What to Ask the AI
- “Can I clarify the input/output format?”
- “Are there constraints on time/space complexity I should aim for?”
- “Can I assume the input is always valid, or should I handle errors?”
- “Should I optimize for readability or performance?”
- “Can I use [specific library/built-in function]?”
- “Would you like me to walk through my test cases?”
What NOT to Ask the AI
Don’t Try To…
- Ask for hints or the solution. “Can you point me in the right direction?” — the AI won’t provide algorithmic hints.
- Ask the AI to debug your code. The AI won’t tell you where the bug is. You’re expected to trace through and find it yourself.
- Ask what test cases are being used. The evaluation criteria are set by the hiring team and not shared during the interview.
- Request a different problem. The problem set is curated for the role. The AI cannot swap questions.
- Ask for your score. Performance feedback goes to the hiring team, not to you during the interview.
Things That Don’t Help
- Coding in silence — the AI can’t evaluate your thought process if you don’t share it
- Writing code without a plan and then trying to fix it retroactively
- Over-engineering the solution when a simpler approach works
- Focusing only on getting the right answer without considering code quality or edge cases
- Asking if the AI has “seen this problem before” or trying to figure out the problem source