Technical Interviews Are Splitting Into AI-Free and AI-Assisted Rounds

Some employers are beginning to test two different signals in technical interviews: whether candidates can solve problems without AI, and whether they can use AI tools without handing over engineering judgment.

There is no standard AI coding interview format yet. Companies can differ on whether AI is allowed, which tools candidates can use, and how much of the evaluation focuses on generated code versus the candidate’s reasoning.

What is becoming clearer is the skill set candidates may need to demonstrate when AI is part of the interview: prompting, reviewing output, finding mistakes, testing assumptions, explaining trade-offs, and taking responsibility for the final solution.

Credit: Photo by mostafa_meraji on Pixabay

What is actually changing in coding interviews

The shift is not simply that companies are suddenly allowing ChatGPT or coding assistants during interviews.

A more useful way to think about it is that employers can now evaluate two different abilities.

One is familiar: can you break down a problem, choose an appropriate approach, write or reason through code, and explain your decisions without relying on a tool?

The other is newer: when AI assistance is available, can you use it without becoming dependent on it?

That means the interview can move beyond:

Can you write this code?

toward questions such as:

Can you tell when generated code is wrong?

Can you improve a weak suggestion?

Can you explain why you accepted one approach instead of another?

Can you test the result before trusting it?

Can you still defend the final implementation yourself?

What we know so far

There is evidence that some employers are experimenting with this model, but it should not be treated as an industry-wide standard.

Arta Finance has described keeping an existing coding interview while adding another round where candidates work in a more realistic codebase with an AI coding assistant.

That is important because it suggests the AI-assisted round is not necessarily replacing fundamentals. It can test a different set of skills.

HackerRank has also argued that interviews involving AI should pay attention to how candidates direct tools, verify results, reason about the output, and maintain ownership of the solution.

The takeaway for candidates is not that every technical interview will now follow this structure.

It is that preparing only for traditional algorithm problems may no longer cover every type of engineering interview you encounter.

When AI is not allowed: fundamentals still matter

If a round does not allow external tools, the evaluation can look much more like a traditional coding interview.

You may still need to:

• break the problem into smaller parts;

• choose suitable data structures;

• explain time and space complexity;

• write clear pseudocode or implementation;

• identify edge cases;

• debug your own mistakes;

• explain trade-offs.

Even as AI tools become part of everyday engineering work, these fundamentals remain useful because they give interviewers a baseline for understanding how you reason without assistance.

Candidates should therefore avoid preparing for AI-assisted interviews at the expense of core technical ability.

When AI is allowed: judgment becomes the signal

An AI-assisted interview changes the nature of the work.

Generating code quickly is not necessarily impressive on its own. The more useful signal is what you do after the model responds.

A strong candidate might explain:

“This implementation works for the basic case, but I want to test what happens when the input is empty.”

Or:

“The generated solution uses this data structure, but for the expected traffic pattern I would rather use this approach because…”

Or:

“Before accepting this function, I want to verify its behavior under concurrent requests.”

That makes your reasoning visible.

The goal is not to demonstrate that you know how to type a clever prompt.

It is to show that AI does not remove your ability to evaluate engineering decisions.

AI-free and AI-assisted interviews can test different skills

AI-free interviewAI-assisted interviewProblem decompositionGiving useful context to the toolAlgorithms and fundamentalsReviewing generated solutionsWriting implementationEditing or rejecting weak outputComplexity analysisChecking assumptionsDebugging your own codeDebugging AI-generated codeExplaining trade-offsVerifying AI recommendationsProducing a working solutionTaking ownership of the final solution

These are preparation categories, not a universal interview sequence. Employers may combine them, separate them, or ignore some entirely depending on the role and their AI policy.

What stronger candidates do after AI generates code

The most important habit is simple:

Treat generated code as a draft.

Do not assume a suggestion is correct because it looks polished.

Review it the same way you would review code written by another engineer.

Ask:

• Does this actually solve the requested problem?

• Which assumptions did the model make?

• What inputs could break it?

• What happens under failure?

• Is the complexity acceptable?

• Are there security or privacy concerns?

• What would I test first?

• What would I monitor after deployment?

Then explain those checks aloud.

That process gives the interviewer something much more valuable than generated code: evidence of your judgment.

How expectations may change by role and seniority

The exact evaluation will depend heavily on the role.

A junior engineer may be expected to show that they can identify an incorrect suggestion, write useful tests, understand the generated code, and explain basic trade-offs.

For more senior roles, the discussion can move toward broader questions:

• Does this design fit the system?

• What happens at higher scale?

• Where are the failure modes?

• How would you observe this in production?

• What would you roll back if something went wrong?

• Is the generated approach maintainable?

Backend candidates may naturally encounter more questions about reliability, concurrency, APIs, data flow, and observability.

Frontend candidates may spend more time discussing accessibility, user experience, browser behavior, state management, and performance.

The specific topics change, but the central principle does not:

you remain responsible for the final engineering decision.

What remains unclear

AI-assisted technical interviewing is still developing.

There is no industry-wide rule covering:

• whether AI tools are allowed;

• which tools can be used;

• whether AI is available for an entire round;

• whether candidates may use their own accounts;

• how generated code is scored;

• whether prompting itself is evaluated;

• how much weight is given to verification versus implementation.

Do not assume AI is allowed simply because you use it in your normal engineering workflow.

Ask before the interview.

A simple question is enough:

Will AI or coding assistants be permitted during the technical interview, and is there anything specific you want candidates to demonstrate when using them?

That removes uncertainty before the clock starts.

How to prepare for both interview styles

Practice solving problems without tools first. Explain your approach, complexity, data-structure choice, edge cases, and testing strategy aloud.

Then repeat some of those exercises with an AI assistant.

But change the goal.

Instead of trying to get the model to produce the solution as quickly as possible, practice reviewing what it gives you.

Ask for a solution, then:

• find a weakness;

• test an edge case;

• change a requirement;

• inspect a generated diff;

• reject a bad suggestion;

• explain why another implementation is better;

• describe how you would monitor the code in production.

This makes AI-assisted practice much closer to engineering work.

If you want to rehearse that under interview pressure, run an AI-assisted mock interview and practice prompting, reviewing output, debugging mistakes, and defending your final solution.

The mistake to avoid

The biggest mistake is not using AI.

It is outsourcing judgment to AI.

A candidate who accepts every generated answer can get stuck the moment the model produces something plausible but incorrect.

A candidate who treats AI as a tool can keep moving.

One habit makes the difference:

Never accept generated code without showing a verification step.

Run a test. Identify an edge case. Explain a trade-off. Say what could fail. Describe what you would monitor.

The coding interview may be changing, but the signal employers ultimately want is familiar: can you understand the problem, make sound engineering decisions, and take responsibility for the result? run a mock interview for this round.