How to Evaluate AI Collaboration Skills in a Coding Interview

When AI use becomes a skill, not a behavior to detect

For most of the last two years, AI in interviews was something to defend against. The rubric was "did the candidate use it, and did we catch them?" That framing still applies in AI-forbidden rounds, and the integrity layer that catches misuse there is real work.

But in the same loop, more and more companies are now running one round where AI is required. Meta, Canva, Shopify, and Coinbase explicitly evaluate candidates on AI collaboration. If your loop is moving in that direction, you need a rubric — because if you tell a candidate "use any tools you want" and score them on whether the final code works, you are measuring almost nothing. The code working is the AI's contribution. The skill is the one the candidate brings to it.

This post is for hiring managers designing that AI-collaborative round. Four dimensions to score, one exercise format, and the common interviewer mistakes that flatten the signal.

The four dimensions worth scoring

A useful AI-collaboration rubric tests skills that are observable in 45 minutes and that distinguish the engineer who uses AI well from the one who copies its output. Four dimensions, with anchors at each level.

1. Prompt quality and problem decomposition

Strong candidates do not ask the model to "build the feature." They decompose the problem first, then prompt for the piece they need help with. The good engineer's first prompt looks like a well-scoped ticket; the weak engineer's looks like the original problem statement.

What you are listening for:

• Did they restate the problem in their own words before prompting?
• Did they constrain the model — file paths, function signatures, the data shape, examples of the expected output?
• Did they ask for one thing at a time, or did they paste the whole brief and pray?

A useful proxy: how many tokens did the candidate write in the prompt for every line of code the AI produced? Below a certain ratio, the candidate is using the model as a magic box and is not exerting judgment on the input.

2. Verification and skepticism

This is the dimension that most often separates strong from weak. The model produces output. What does the candidate do with it?

The strongest signal is whether the candidate verifies before integrating. Specific behaviors:

• Reading the generated code line by line before pasting it
• Running it against a test case the candidate constructed (not one the model suggested)
• Catching a hallucinated function or library and either re-prompting or replacing it themselves
• Noticing that the output is plausible but wrong on a corner case

The weakest behavior is paste-and-pray: the candidate copies the model's code into the editor, runs the happy path, sees green, and moves on. This is the AI-collaboration equivalent of a junior engineer merging without reading the diff.

Use the same code coherence analysis that flags LLM-generated code in async assessments. In an AI-required round, the score is not "did the candidate use AI" — that is expected. The score is whether the candidate's edits to the AI output show evidence of reading and thinking, or whether the diff is pure paste.

3. Control over the loop

Junior candidates follow the AI wherever it leads. Strong candidates lead the AI.

Concrete behaviors to score:

• When the model returns a wrong answer, does the candidate diagnose and reprompt with a corrected constraint — or do they keep regenerating, hoping for a different answer?
• When the model proposes an architecture the candidate disagrees with, do they push back ("I'd rather use a state machine here, can you re-do this with that pattern?") or do they accept the suggestion?
• When the model goes off on a tangent (refactoring something the candidate did not ask about), does the candidate notice and pull it back?

This dimension is invisible without screen recording. Make sure the round captures the prompt history and the editor diff together, not just the final code.

4. Communication while pairing with AI

The last dimension is whether the candidate keeps the interviewer in the loop. This sounds soft but it is the highest-signal predictor of how the candidate will work day-to-day on a team that pairs with AI.

What good looks like:

• Narrating before prompting: "I am going to ask Claude to draft the parser. I expect it to get the tokenizer right but probably mishandle the escape characters — I will need to fix that."
• Showing the prompt before sending it
• Surfacing disagreement with the model out loud, not silently
• Distinguishing what they wrote from what the model wrote when explaining the code

The weak version: silent prompting, long pauses while the candidate reads the model's output without sharing what they think of it, code appearing in the editor with no explanation of where it came from. A candidate who codes silently with AI will pair silently with humans, and that is a team-dynamics signal you want to catch in the loop.

The exercise that surfaces these skills

The temptation is to give the candidate an algorithm problem and let them use AI. Do not do this. The AI will solve it in seconds and the candidate will spend 40 minutes editing comments. You will learn nothing.

A better exercise has three properties:

1. Multi-step, with a built-in surprise. Start with a small task the candidate can clearly solve. Halfway through, introduce a change request that breaks an assumption — a new data format, a performance constraint, a deprecated library. The adaptation is where the AI-collaboration skill shows up.
2. A subtly broken starting point. Give the candidate code that compiles and runs but has a subtle bug — an off-by-one, a swallowed exception, a race condition under load. Watch whether they catch it themselves or trust the AI to find it. Most models will not find a subtle bug unprompted.
3. A documentation or research component. The task requires the candidate to integrate with an unfamiliar library or API. The model knows about it. The candidate has to verify what the model says against the actual docs. The gap between "model claims" and "docs say" is rich signal.

Forty-five minutes for the whole round. ClarityHire's collaborative code editor runs Monaco with integrated execution and pairs it with prompt-history capture, so the interviewer can see the editor diff and the model conversation side-by-side during the debrief — not just the final code.

Scoring anchors, 1 to 4

Score each dimension independently on a 1–4 scale. Anchor the levels to behaviors, not adjectives.

Prompt quality (1–4):

• 1: Pastes the entire problem statement into the model; no decomposition.
• 2: Asks for the feature as a whole, but adds at least one constraint.
• 3: Decomposes the task into two or three pieces; prompts each separately with relevant context.
• 4: Writes well-scoped prompts that include the function signature, the data shape, and a worked example.

Verification (1–4):

• 1: Pastes model output without reading.
• 2: Reads the output but only tests the happy path.
• 3: Constructs at least one test case independent of the model's suggestions; catches at least one issue.
• 4: Treats every output as suspect; verifies against real docs, real test data, and corner cases before integrating.

Control (1–4):

• 1: Follows whatever the model produces; regenerates rather than reprompts on failure.
• 2: Reprompts with the same context after failure; sometimes catches the model going off-track.
• 3: Diagnoses why the model is wrong and reprompts with a corrected constraint.
• 4: Sets the architecture themselves and uses the model for tactical pieces; pushes back when the model suggests a different design.

Communication (1–4):

• 1: Silent prompting; output appears in the editor unexplained.
• 2: Narrates the final result but not the process.
• 3: Narrates before prompting and shows the prompt to the interviewer.
• 4: Distinguishes their own contribution from the model's clearly; surfaces disagreements with the model out loud.

Use a structured scorecard where each interviewer locks their score before the debrief. The dimensions are independent enough that disagreement is informative — one interviewer might rate the candidate 4 on verification and 2 on communication, and that pattern is the thing worth discussing.

Common interviewer mistakes

Five errors that flatten the signal in this round:

1. Grading the final code. The code is the AI's. The judgment, prompts, and edits are the candidate's. Score those.
2. Asking the candidate to "explain what the AI did." That tests reading comprehension, not collaboration. Instead, ask "what would you change about how the AI approached this?"
3. Letting the round run silent. If the candidate goes quiet for two minutes while reading model output, prompt them: "talk me through what you are looking at." Silence is not the test; you are evaluating how they pair.
4. Picking a problem the model can one-shot. A trivial problem means the candidate never has to demonstrate control, verification, or recovery. Pick a problem that requires at least one round of disagreement with the model.
5. Forgetting the integrity baseline. This is the AI-required round, but you are still in the same loop as the AI-forbidden round. If a candidate's keystroke pattern in this round looks identical to the forbidden round, that is interesting — both for good reasons (the candidate genuinely types this way) and bad ones (the same off-camera helper is running both rounds).

What to do next

If you are about to add an AI-collaboration round to your loop:

1. Decide which of the four dimensions matter most for the specific role. An applied ML engineer probably weights verification highest; a generalist software engineer might weight communication.
2. Design one exercise that includes a mid-round surprise and a subtle starting bug. Write the anchors before the first candidate sees it.
3. Capture the prompt history alongside the editor diff. Without the prompts, you are grading code; with them, you are grading the engineer.
4. Calibrate with two interviewers on a recorded session before running it live. The disagreement between them is the rubric.
5. Score this round independently from the AI-forbidden round. They measure different things, and conflating them will reward the wrong candidate.

The goal of this round is not to find candidates who can use AI — almost all candidates can. It is to find the ones whose judgment, verification, and direction are good enough that the AI's leverage becomes real, instead of expensive paste-and-pray.