AI-Enabled Coding Interviews: Turn Model Answers Into Test Cases

The useful skill in an AI-enabled coding interview is not producing the cleverest prompt. It is turning a model's suggestion into a claim you can try to break. Give yourself one small function, one edge case the first answer is likely to miss, and one test that makes the result visible. That is a 20-minute drill you can repeat before the real round.

Some companies are experimenting with interviews where an assistant is part of the environment. That changes the signal. A candidate who can ask a model for a patch but cannot identify the patch's assumptions has not demonstrated much engineering judgment. The stronger move is to narrate a short verification loop: what the answer claims, the smallest input that challenges it, and the evidence that decides whether to keep it.

This is not a recipe for using tools where they are not permitted. Check the instructions for the round first. When a tool is explicitly part of the exercise, use it like you would use a teammate's code review: useful, fallible, and accountable.

Why does a plausible answer need a counterexample?

A generated solution often looks complete because it handles the happy path cleanly. Interviews become interesting at the boundary: empty input, duplicate work, a zero value, a stale response, a mutation that callers did not expect.

The fastest way to inspect an answer is to turn its central promise into one sentence:

Model claim	Smallest challenge	Evidence to collect
"This deduplicates records by ID."	An ID of 0; numeric 1 and string "1"	Returned IDs, input immutability
"This handles invalid rows."	null or an object with no id	A clear, intentional error
"This preserves first-seen order."	The same ID appears in both lists	Exact output sequence

The table takes less than two minutes to make. It also gives you a much better explanation than "the model said this should work."

A runnable 20-minute drill

Imagine an interviewer asks you to merge two event lists. You ask an assistant for a dedupe implementation. Before accepting it, define the contract:

1. Each row must be an object with its own id.
2. The first row for an ID wins.
3. IDs are type-sensitive: 1 and "1" are different IDs.
4. The input arrays must not be mutated.
5. Invalid rows should fail loudly rather than disappearing.

Here is a dependency-free Node.js version with tests. Save it as merge-events.test.js and run node merge-events.test.js.

const assert = require("node:assert/strict");

function rowKey(row) {
  return `${typeof row.id}:${String(row.id)}`;
}

function mergeFirstSeen(left, right) {
  const seen = new Set();

  return [...left, ...right].filter((row) => {
    if (
      row === null ||
      typeof row !== "object" ||
      !Object.hasOwn(row, "id")
    ) {
      throw new TypeError("Every row needs its own id");
    }

    const key = rowKey(row);
    if (seen.has(key)) return false;

    seen.add(key);
    return true;
  });
}

const left = [
  { id: 0, source: "left-zero" },
  { id: 1, source: "left-number" },
];
const right = [
  { id: "1", source: "right-string" },
  { id: 0, source: "right-duplicate" },
  { id: 2, source: "right-new" },
];

assert.deepEqual(
  mergeFirstSeen(left, right).map((row) => row.id),
  [0, 1, "1", 2],
);
assert.deepEqual(left, [
  { id: 0, source: "left-zero" },
  { id: 1, source: "left-number" },
]);
assert.throws(
  () => mergeFirstSeen([{ label: "missing id" }], []),
  /needs its own id/,
);

console.log("contract holds");

The important line is not the Set. It is the test data. A truthiness check such as if (!row.id) would silently lose the valid 0 record. A coercive key could collapse 1 and "1". Neither problem is obvious from a tidy-looking implementation.

Notice the use of Object.hasOwn rather than a truthiness check. It asks whether the property is actually present, even when its value is 0, false, or an empty string. That is exactly the distinction this contract needs. The MDN reference for Object.hasOwn is worth bookmarking for these boundary checks.

How should you use the drill in an interview?

Do not read the whole test file aloud. Use a four-part narration:

1. State the invariant. "First-seen order matters, and I want numeric and string IDs to remain distinct."
2. Name the risk. "The tempting implementation is likely to mishandle a falsy ID or coerce keys."
3. Show one discriminating test. "I would include 0, 1, and "1" before trusting the helper."
4. Explain the trade-off. "This is in-memory and linear time for this call. At a database boundary, I would enforce the identity rule there as well."

That answer communicates more than algorithm recall. It shows that you can establish a contract, choose evidence, and describe where the current solution stops being sufficient.

What should you ask the model?

Specific questions create inspectable output. Avoid "write the best merge function." Instead try:

Implement a pure JavaScript function that merges two arrays, preserves first-seen order, distinguishes numeric from string IDs, rejects rows without an own id, and list three edge cases your code might still miss.

Then verify the model's response against your table. The assistant may offer a good implementation, but it can also expose an assumption you did not think to test. Either way, you leave with a stronger solution.

A useful follow-up is: "Which test would fail if the function used truthiness to validate an ID?" This forces the discussion from general confidence to a falsifiable prediction. If the answer cannot predict the failing test, it has not earned your trust.

What changes when the assistant is officially allowed?

The task is still engineering. CoderPad's discussion of AI-enabled interviews makes the case for evaluating work that resembles the job: investigate unfamiliar code, use tools deliberately, and justify decisions. That makes the verification step visible rather than optional.

A practical session plan is:

• Minutes 0-4: read the code and restate the contract in your own words.
• Minutes 4-9: ask for one narrow implementation or explanation, not an entire architecture.
• Minutes 9-15: write two counterexamples that distinguish a robust answer from a merely plausible one.
• Minutes 15-20: run the tests and practice a 60-second explanation of the result.

Practice tools such as aceround.app — an AI interview assistant can help create follow-up pressure before a live conversation. The goal of the practice is still the same: be able to defend the evidence in your own words.

FAQ

Is prompt quality more important than coding in an AI-enabled interview?

No. A clear prompt helps, but it is only the start. The stronger signal is whether you can identify the assumptions in the output and design a small test that would prove the answer wrong.

Should I write tests for every line during a timed round?

No. Choose tests with high information value. One falsy ID, one type-boundary case, and one invalid input reveal more than a long list of normal examples.

What if the model's answer passes my first tests?

Explain what the tests establish and what they do not. For this example, the helper is linear in the number of rows and deliberately in-memory. A production system may need a database uniqueness constraint, a memory limit, or a policy for later updates.

Can I use this workflow when tools are not explicitly allowed?

Use the verification mindset everywhere, but follow the interview's stated tool policy. The method does not depend on a model: turn a proposed solution into a contract, try to break it, then explain the evidence.

Sources and disclosure

• CoderPad, AI in the interview is not cheating; it is the job.
• DEV Community, Guidelines for AI-assisted articles.

Disclosure: This article was drafted with AI assistance and reviewed for technical accuracy.