AI-assisted coding interviews test a different skill. Here's how to practice it

Abdullahhzg — Tue, 06 Oct 2026 06:10:04 +0000

More companies now let candidates use an AI agent during coding interviews. That sounds like it makes things easier. It mostly changes what's being measured.

In a classic LeetCode round, the question is "can you produce a correct algorithm from scratch?" In an AI-assisted round on a real codebase, the questions become:

Can you find your way around code you've never seen?
Can you give the agent the right context instead of dumping errors at it?
Do you actually verify what it changed, or do you accept the first diff?
Do you run the tests, and how often?
Do you burn your whole token budget on vague retries?

Here are the habits that seem to matter most, and how to practice them.

1. Read the issue and the existing tests before you prompt

The fastest way to waste an agent is to paste the issue title and say "fix this". Spend the first few minutes yourself: find the module the issue touches, skim the nearby tests, and form a guess about where the bug lives. Then your first prompt can point the agent at the right file with a hypothesis.

2. Give context, not raw stack traces

Pasting a 60-line traceback with "why?" usually gets you a confident guess. A better prompt says what you ran, what you expected, what happened, and which file you think is involved. You'll get fewer, better attempts.

3. Treat every diff like a code review

Agents happily "fix" a failing test by special-casing the input, or change behavior somewhere you didn't ask. Read each diff. Ask yourself whether it fixes the root cause or just the symptom.

4. Run tests early and often

Reproduce the bug with a test before the fix, then run the relevant tests after every meaningful change. In a timed round, a fast feedback loop beats a long conversation with the agent.

5. Watch your budget

Many AI-assisted rounds have a time limit, and some track token use. Retry loops ("still broken, try again") are where budget disappears. If two attempts fail, stop and re-read the code yourself.

Practicing this for real

The hard part is that LeetCode doesn't exercise any of this. You need a real repo, a real bug and tests you can't see.

That's why we built PraxisAI. It takes real GitHub issues from SWE-bench (Django, Flask, SymPy, matplotlib and more) and checks each repo out at the commit where the bug still existed. You work in browser VS Code with an AI agent, a timer and a token budget, and hidden tests from the real upstream fix decide whether you solved it.

Every graded round gets a free score, verdict and test outcome (2 free full-model rounds a week, no card). Paid plans add a breakdown across six dimensions, a prompt coach that flags habits like raw-error pastes and vague retries, and a replay of every prompt and agent action.

If you're preparing for this interview format, I'd love to hear which of these habits you find hardest, and what you'd want feedback on after a round.