Everyone is hiring for AI fluency. Almost nobody agrees on what it means.
This piece answers the most common questions engineering and talent leaders are grappling with right now, based on conversations with hiring teams at global technology companies navigating this shift.
What is AI fluency in software engineering?
AI fluency is a software engineer’s ability to work effectively with AI tools — not just use them, but direct them, evaluate their output, and know when not to rely on them.
It is not familiarity with a specific tool like GitHub Copilot or Claude. Fluency at that level becomes outdated within months. What persists is the underlying capability: clear prompting, critical evaluation of AI-generated output, knowing when the model is wrong, and knowing when to override it.
A useful frame: an AI-fluent engineer can treat an AI agent the way a skilled engineering manager treats a capable junior developer. They don’t just accept what’s produced. They direct, review, push back, and improve.
How is AI fluency different from knowing how to use AI tools?
Tool use is a behavior. Fluency is a judgment capacity.
A candidate who knows how to open Copilot and generate a function is using an AI tool. A candidate who reads the output, identifies an edge case the model missed, refactors the logic, and explains why they made each change is demonstrating AI fluency.
The distinction matters for hiring because the behavior is easy to fake or game. Judgment, especially under questioning, is much harder to simulate.
Why is it so difficult to define AI fluency for interviews?
Because the industry hasn’t converged on what exceptional performance looks like yet.
Engineering leaders have described the same problem: their own engineers disagree on what AI-fluent performance looks like, which makes building consistent evaluation rubrics extremely difficult. The instinct is to add more criteria. But when the underlying capability is still being defined, rubric complexity doesn’t resolve the ambiguity. It just makes the process more complicated.
The more productive approach seems to be defining the behaviors that signal judgment rather than defining fluency abstractly. Does the candidate check what the AI produced? Do they articulate why? Can they improve it? Those are observable behaviors, even if “AI fluency” as a concept remains contested.
Should interviews require or ban AI use?
Require it.
The instinct to ban AI in technical interviews made sense in 2022, when the concern was that candidates would use AI to complete assessments dishonestly. That logic has inverted. If the job involves working with AI tools all day, an interview that forbids AI use is assessing a skill the candidate won’t be using.
The more defensible and predictive approach is to require AI use and evaluate how the candidate uses it. Does their prompting show clear thinking? Do they verify output? Do they catch what’s wrong? Those signals are meaningful. “Candidate completed the problem” without those signals is not.
Some organizations have gone further: designing interview problems that are deliberately too complex to complete in the allotted time without AI assistance. The evaluation is not whether the candidate finishes. It is how they work under conditions that mirror the actual job.
What is the Plan/Build/Review framework for technical interviews?
Plan/Build/Review is a structure for technical interview rounds built around how AI-assisted engineering actually works.
Plan: The candidate receives a real-world task in a code repository and works through how they’d approach it — clarifying requirements, identifying edge cases, breaking down the problem. This is where prompting strategy and engineering judgment show up before any code is written.
Build: AI handles a significant portion of code generation. The candidate’s role is to direct the agent and manage the output.
Review: The candidate evaluates what was generated — reading the pull request, identifying issues, writing comments, pushing the output further. This is the phase most closely analogous to what senior engineers spend the majority of their time doing.
The insight behind this structure is that the plan and review phases carry more signal about engineering judgment than the build phase does. An engineer who plans clearly and reviews rigorously is demonstrating the capability that matters most in an AI-augmented environment.
Are coding fundamentals still relevant for hiring?
Yes — and they may matter more now, not less.
Several engineering leaders have observed that the engineers who get the most out of AI tools tend to be the ones with the strongest underlying fundamentals. Understanding algorithms, data structures, system design, and code quality makes it easier to evaluate what an AI agent produces, catch what’s wrong, and direct it more precisely.
Fundamentals are shifting from being the primary signal in a technical interview to being a prerequisite for demonstrating AI fluency. The evaluation question is no longer “can this person write correct code.” It is “does this person understand code well enough to evaluate and improve what AI generates.”
How do we assess AI fluency without introducing new bias?
With the same rigor applied to any evaluation methodology: structured criteria, consistent delivery, bias audits, and interpretable scoring.
The risks are real. Any evaluation methodology can amplify bias if it’s inconsistently applied, if the criteria are unclear, or if scoring is opaque. AI-powered assessment tools introduce additional questions: Is the model’s evaluation consistent across demographic groups? Is scoring explainable? Can a hiring team understand why a candidate received a particular score?
The standard worth holding any AI assessment tool to: Is the scoring interpretable enough that a hiring decision can be explained and defended? Is there a human in the loop before any rejection? Is the tool being audited for bias on an ongoing basis, not just at launch?
European organizations are navigating additional regulatory requirements around automated decision-making, which adds further requirements for transparency and human review. Assessment tools operating in EMEA need to be designed with these requirements as a baseline, not an afterthought.
How is integrity maintained in AI-permitted assessments?
By shifting from static to dynamic evaluation.
A static take-home assessment — a fixed problem with a fixed answer — becomes increasingly gameable as AI tools improve. A candidate who submits a perfect solution tells you very little if the solution could have been generated entirely by an AI model with no understanding from the candidate.
Dynamic assessment — where the evaluation includes conversation, follow-up questions, and probing into reasoning — is much harder to game. A candidate can submit AI-generated code. They cannot easily demonstrate understanding of why the code works, what its edge cases are, and how it would need to change under new constraints if they don’t actually understand it.
Integrity measures including session monitoring, browser activity detection, and identity verification remain relevant, but they are backstops rather than the primary mechanism. The primary mechanism is an evaluation format where understanding is what’s being assessed, not just output.
What should AI fluency interview questions actually look like?
Less “solve this algorithm” and more “here’s something that was built — what’s wrong with it, and how would you improve it.”
Useful signal comes from questions like:
- “Walk me through how you’d plan this task before writing any code.”
- “Here’s the output the AI produced. What do you notice? What would you change?”
- “I’m adding a new constraint to this problem. How does your approach need to change?”
- “Why did you prompt it that way? What were you expecting?”
These questions assess reasoning, not just output. They’re harder to prepare a canned answer for, and they more closely mirror what strong AI-fluent engineering actually looks like.
HackerRank builds technical hiring tools for the agentic era, including AI-powered screening, next-generation pair programming environments, and interview frameworks designed around Plan/Build/Review. Learn more at hackerrank.com.