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In a recent post, we walked through the data on how fast AI fluency has moved from nice-to-have to baseline hiring expectation: job postings requiring AI skills up 144% year-over-year, and workers with demonstrated AI skills pulling wage premiums as high as 62%. But knowing you need to hire for it and knowing how to actually verify it in an interview are two very different problems.

Most hiring processes haven't caught up. According to 24 Seven's 2026 Salary Guide (a survey of over 2,000 marketing, creative, and technology professionals), assessing genuine AI skill level now ranks as the single biggest hiring challenge companies report. Not compensation. Not competition for candidates. The actual difficulty of telling a fluent candidate from one who's memorized the right vocabulary.

That difficulty is self-inflicted, though. Most interview processes are still asking a question that was never going to produce a useful answer.

"Have You Used Copilot" Predicts Nothing About Skill

Every candidate has used an AI coding tool at this point, so asking whether they have is like asking a 2026 candidate whether they've used a text editor. A yes/no question about tool exposure can't distinguish between someone who occasionally accepts an autocomplete suggestion and someone who has fundamentally changed how they approach a problem because of what AI-assisted workflows make possible.

Worse, it's a question candidates know how to answer well regardless of actual skill. "Yes, I use it daily" is now the safe, expected answer, which means it's stopped being a signal at all.

AI Fluency Is Judgment, Not Tool Exposure

AI fluency isn't a single skill, it's a judgment call made repeatedly: knowing when to reach for AI assistance, when not to, how to direct it toward a good outcome, and, critically, how to catch it when it's confidently wrong. That last part is the one most interview processes miss entirely, and it's the part that actually predicts whether someone will ship a subtle bug straight from an AI suggestion into production.

That's also roughly the model behind BetterEngineer's own AI-fluency assessment, which we run on every AI-focused candidate before a client ever sees a resume: fluency is treated as a spectrum with real gradations, not a checkbox, because "used the tools" and "changed how they think and build" are very different candidates who'd both check the same box on a resume.

Four Interview Techniques That Actually Reveal Fluency

1. Pair on a real problem with AI tools live, and watch the review, not the code.

Don't ask about their process. Put them in front of an actual bug or feature with their AI tool of choice open, and watch what they do with what it suggests. A fluent engineer treats the output as a first draft: they scan it critically, catch the parts that don't fit the codebase's conventions, and adjust before accepting. A less fluent one accepts quickly and moves on. The code quality at the end matters less than what you can see them doing in between.

2. Ask for a specific time they rejected an AI suggestion, and why.

This is the single best low-effort question in this list, because it can't be answered well without real experience. Someone who's actually worked this way has a specific story: a suggestion that looked right but broke an edge case, introduced a security issue, or didn't match an architectural decision made for a reason the tool couldn't know about. Someone who hasn't will give you something vague ("I always review the code carefully") because they don't have a real instance to point to.

3. Ask how they structure a prompt when debugging something hard, not whether they "use AI for debugging."

Fluent engineers have opinions here: providing the right context, being specific about constraints, iterating on a prompt rather than accepting the first response, and knowing when to abandon the AI approach and read the actual stack trace themselves. If the answer is "I just describe the bug and see what it says," that's a meaningfully different skill level than someone who can walk you through an actual technique.

4. Ask what they do before an AI-assisted change merges.

This surfaces the safety-net habits that separate fast-but-risky from fast-and-reliable: do they run the existing test suite, add new tests for the AI-generated logic specifically, run a security/dependency scan, or read the diff line by line before approving their own PR? An engineer who's fully outsourced their judgment to the tool usually can't answer this in any detail, because they haven't had to.

Code They Can't Explain Is the Real Red Flag

Watch for candidates who can't explain a piece of AI-generated code without regenerating or re-prompting to get the explanation. If someone can't walk through what a function does, why it does it that way, and what would break if a specific line changed, without asking the AI to explain it back to them, that's not fluency. That's dependency. It will look identical to fluency on a fast take-home exercise and look completely different three weeks into a real project, when they hit a bug the AI can't resolve for them.

Turn These Questions Into a Scorecard, Not Just Talking Points

Good questions still produce inconsistent hiring if every interviewer is grading the answers in their head, against their own private bar. The teams that get the most out of this approach turn it into a shared rubric before the first interview happens, not after a hire goes sideways.

A simple three-tier scale per technique works better than a pass/fail gut call. For the "rejected a suggestion" question, for instance: not yet fluent looks like a vague or hypothetical answer with no specific story; working fluency looks like a real story, but one where the catch was a large, obvious error; fully fluent looks like a real story where the catch was subtle, something a less experienced engineer would likely have shipped. Write two or three sentences like that for each of the four techniques above, hand it to every interviewer on the loop, and debrief against the same rubric instead of a general impression.

The specific wording of the tiers matters less than the fact that everyone on the panel is using the same ones. Without that, two interviewers can watch the same candidate, form opposite conclusions, and neither one is technically wrong, because "fluent" was never defined the same way twice.

You Don't Need New Tools, Just a Better Process

None of these techniques require new tooling. They just require restructuring the interview around judgment and process instead of tool-name recognition. If you're hiring directly, even swapping one generic "tell me about your experience with AI tools" question for the rejection-story question above will surface more signal than the rest of the interview combined.

If you'd rather not build and calibrate this assessment yourselves, it's exactly what we do before any AI-fluent engineer reaches your team. See how BetterEngineer evaluates AI-fluent engineering talent, or get in touch to talk through what your team specifically needs to screen for.