The AI Coding Round Techniques

More and more companies are now adding a new AI-enabled coding round — Meta, Stripe, Shopify, Rippling, Canva, and LinkedIn, among others.

The common setup is a platform like CoderPad with a built-in assistant, or a company's own coding platform with AI baked in (bringing your own public AI IDE is still uncommon).

So how should you approach this round, and how is it different from a normal coding interview? What's your strategy?

The simple approaches

Approach A — ignore the AI and hand-code it.

We understand. Meta's coding rounds, for example, are sometimes just hard algorithm questions — and you may even find it faster to code them by hand. But then you never demonstrate your AI skills.

Approach B — let the AI write everything, retry on every failure, and spend most of the interview only talking to the AI.

Now you're not showing your coding skills — your problem-solving, how you handle edge cases, how fast you can code.

Prepped a pile of LeetCode? It's wasted here — you never get to show it.

The reality: the round isn't standardized

Different companies may want different things from this round, and they usually won't tell you which.

At one extreme it's just LeetCode: you're still expected to drive the algorithm, and the AI is only there for fast web search or autocomplete.

At the other, you're handed total freedom — and quietly judged on how well you use the AI tools and how deeply you understand the systems behind them.

The reality is, nobody has agreed on what "good AI use" even means in an interview.

If the round is really testing raw problem-solving, a plain LeetCode question would do and the AI is pointless.

If it's testing whether you can drive AI well — learn fast, keep good habits, act like a strong teammate — that's a different game with different rules.

This article is about that second game.

For the second case, this is the plan

1. You write a prompt, with constraints.
2. The AI writes a solution.
3. You review it — is this actually right?
4. If it's wrong, you steer: say exactly what's off and re-prompt with new information. Then repeat.

The AI owns step 2. You own review and steer — and that's what's being scored.

• Each round gets closer to correct → you converge → pass.
• Each round is a random restart → you spin → fail.

The playbook

Step 1 — Before you ask the AI

Don't prompt first. Work out for yourself what a correct solution looks like: the approach, the complexity, the tricky cases. This is your answer key — without it, you literally cannot grade what the AI hands back.

Take "find the median of two sorted arrays."

If you already know the brute force is merge-and-index at O(m+n), the intended answer is a binary search at O(log) — then when the AI returns a binary search, you go straight to the boundary and catch the bug. Without that picture, the same off-by-one slips through: it passes two of three tests, you shrug, and you regenerate.

Step 2 — Write it down and walk the interviewer through it

Here's the move most people skip: take your sketch of the answer, type it into the doc as a short comment, then say it out loud.

Why this scores well:

• It shows good coding practice — leaving intent in comments, following convention, working like a real teammate. Reads as experience.
• It shows your reasoning before any code exists — the interviewer sees you thinking, not just typing.
• It becomes the checklist you grade the AI against in Step 4.
• If your sketch is slightly off, the interviewer can nudge you now, which reads as collaborative.

Silent candidates get no credit for thoughts they never surfaced. Make it visible.

Step 3 — Go test-driven: you define the interface, AI fills in the tests

Before asking for a solution, pin down the interface and the behavior, in writing:

Function: meeting_rooms(meetings: list[tuple[int,int]]) -> int
- input: list of (start, end), up to 100k items
- empty list -> 0
- a meeting ending exactly when another starts: NO new room
- must beat O(n^2)

Now let the AI generate test cases from that spec — it's fast and fine for breadth. The point is that you set the contract first, so the tests anchor to your definition of correct, not the AI's guess.

Step 4 — Review the test cases yourself

Letting the AI write tests is fine for speed. Letting it be the judge of its own code is not: the same blind spot that produced the bug will produce a test that steps around it, so everything goes green and nothing is actually verified.

So you read the generated cases and add the ones the AI quietly skipped — the reversed input, the empty list, the value that overflows. Those are exactly where a planted bug hides, and adding them is the whole job of this step.

Step 5 — Write a real prompt: a spec, not a signature

// BAD
"Write a function to find duplicate user IDs in a list."

-> nested-loop O(n^2). Fine for 100 users. Dies at 10 million.

// STRONG
"Find duplicate user IDs.
 - Input: up to 10M IDs, streamed from a file, won't fit in memory
 - Time: must be O(n)
 - IDs may be null - skip them
 - Return each duplicate once"

-> hash-set / streaming solution that survives production.

Same AI. Completely different output. The constraints — size, edge cases, performance — did all the work.

A reusable prompt template:

Task:        <one line - what to build>
Input:       <types + size, e.g. up to 1e6 items>
Constraints: <time/space budget, memory limits>
Edge_cases:  <empty, duplicates, nulls, reversed, overflow...>
Output:      <exact shape - "return each dup once, as a list">
Example:     <one input -> expected output>

That last line matters: one concrete input -> output example (few-shot) steers harder than a paragraph of description.

Step 6 — Write your own driver; don't trust only the grader

A driver is just a few throwaway lines — a tiny main you write in the same editor — that call your function on inputs you choose and print what comes back. You run it yourself instead of relying on the platform's pass/fail.

The grader says:

2 / 5 tests failed.   // no detail. Not good.

Your driver says:

for x in [[], [1], [1,1,2], [2,1], list(range(10**6))]:
    print(x[:3], "->", dedupe(x))

# [] -> None          <- there it is. crashes on empty.
# [1] -> [1]
# [1,1,2] -> [1,2]

Opaque red X → located bug → one-line fix, in 30 seconds. And the interviewer just watched you verify independently instead of praying to a black box.

What to feed it. Pull the inputs straight from your Step 1 answer key and your Step 4 review — the empty input, a single element, duplicates, a reversed or unsorted case, and one max-size input to expose anything quadratic. Print the actual return value next to what you expected, so a mismatch jumps out instead of hiding behind a pass/fail.

How to run it in the room. Narrate as you go — "let me run a couple of cases by hand" — then read the output out loud and react to it. That turns debugging into something the interviewer can watch, which reads as methodical rather than lucky. When the driver disagrees with your expectation, you've found either a bug in the code or a gap in your own understanding — both are worth catching before you submit.

Keep it disposable. A driver is scaffolding, not the deliverable. Leave it clearly separated from your solution (or delete it) before the final submit, so it's never mistaken for the answer.

Some red-alert moves

1. "Try again" with no new information — hitting regenerate, or typing "nope, redo," without saying what's wrong. That treats the AI like a slot machine: no information goes in, so nothing converges out. It's pure luck, and it's obvious from the outside.

2. Copying without reading — pasting AI code straight into the answer. You can't catch what you never read, so it signals zero independent judgment, the one thing the round exists to measure.

3. Stopping the moment tests go green. Green isn't the same as correct, especially when you never vetted the tests yourself (Step 4). Stopping there says you accepted the surface and never looked underneath.

4. One giant, vague prompt — dumping the whole problem in a single unstructured ask, no constraints, no example, then hoping the AI guesses right. Specificity and one concrete example are the two highest-leverage things you can add; skipping them is choosing a worse first draft on purpose.

5. Never breaking the problem down — demanding the entire solution in one shot instead of chaining smaller prompts (parse the input → core logic → edge cases). Big monolithic asks are where the AI invents structure that doesn't hold; decomposing keeps each step small enough to actually check.

Next Post

This AI Native Interview round is becoming more and more popular. Teams are leaning on AI for speed while counting on engineers to catch what it gets wrong.

Do you know, based on research, only about 29% of developers trust AI output?

Follow our weekly feed — upcoming pieces dig into the prompt-craft itself:

• How do you write good examples in Prompts?
• What does "done" actually mean?
• How to Iteration as the core skill?

Stay tuned!

References & further reading

• Anthropic — Be clear, direct, and detailed
• Anthropic — Prompt engineering overview — the full set of techniques, from being specific to using examples.
• OpenAI — Prompt engineering guide — a complementary take on writing effective prompts.

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