The expert guide for running technical interviews

Technical interviews have traditionally been designed around a constraint that no longer exists: candidates had to do almost everything themselves.

That made the format relatively straightforward. Give someone a problem, watch them write code, ask a few questions, and determine whether they know what they’re doing. Today, candidates increasingly work alongside AI in their actual jobs, which means an interview that prohibits every modern development tool can be just as artificial as one that lets a candidate delegate the entire interview to an agent.

The challenge is further complicated by something that has always been true about interviews: interviewers are inconsistent. One interviewer helps too much, another barely speaks, and a third rejects an otherwise excellent candidate because they didn’t arrive at the interviewer’s preferred solution. Companies then aggregate these subjective observations into a seemingly objective hiring decision.

This is a measurement problem disguised as an interviewing problem. A technical interview should generate evidence that improves a hiring decision, not simply create another hurdle for candidates to clear.

Fortunately, the format is becoming much more flexible. On Coderbyte, interviews can now happen inside a collaborative environment with video, coding challenges, multi-file projects, spreadsheets, Jupyter Notebooks, whiteboards, and VS Code. Candidates can even work alongside AI when doing so reflects the actual role.

The tools have changed. The objective hasn’t. Here’s how to run technical interviews that generate useful signal.

1. Know what you’re trying to learn

The biggest mistake in technical interviewing happens before the interview starts: companies schedule one without defining what information they expect to get from it.

“Technical ability” is not a skill. It’s a category containing dozens of skills that matter differently depending on the job. Debugging, system design, algorithmic thinking, database architecture, code quality, communication, product judgment, and AI fluency can all be important, but few roles require each in equal proportions.

Before deciding what question to ask, define what uncertainty you’re trying to resolve. If an assessment already demonstrated that a candidate can write an efficient algorithm, having them solve another algorithm during an interview provides diminishing information. Instead, use the interview to explore something you couldn’t confidently measure asynchronously.

For example, ask the candidate to debug an unfamiliar codebase while explaining their thinking. Give them an ambiguous requirement and see what questions they ask before implementing it. Ask them to design a system on a whiteboard and introduce new constraints halfway through. Or give them imperfect AI-generated code and ask them what they would change.

Coderbyte supports this by allowing teams to create interview templates containing multiple questions and collaborative environments. The point of a template isn’t simply convenience. It forces you to decide in advance what information each part of the interview is supposed to generate.

If you can’t explain what a question will teach you about the candidate, don’t ask it.

2. Stop treating interviews like exams

A traditional coding interview is strangely adversarial. One person knows the answer, the other person tries to discover it, and everyone pretends this resembles software development.

Sometimes it does. Usually it doesn’t.

Most engineering work involves incomplete requirements, existing code, documentation, libraries, coworkers, search engines, terminals, debuggers, and increasingly AI. Developers rarely spend their days implementing algorithms from memory while another developer silently watches them.

This doesn’t mean algorithmic interviews are useless. If algorithmic reasoning is important for the role, test it. But interview environments should increasingly resemble the environments where candidates will actually work.

Coderbyte’s VS Code interview environment can load public or private GitHub repositories, run multiple terminals, install arbitrary packages, launch front-end and back-end servers, and support libraries such as Selenium and Playwright. That makes it possible to interview someone by asking them to work inside an actual application rather than an empty text editor.

The question changes from “Can you produce the answer?” to “How do you work toward an answer?”

That’s usually the more valuable thing to know.

3. Interview the process, not just the output

Two candidates can arrive at exactly the same working solution and reveal dramatically different levels of ability along the way.

One might immediately start coding, repeatedly hit dead ends, and eventually stumble into something that passes. Another might clarify the requirements, identify the important constraints, consider several approaches, explain the tradeoffs, and deliberately choose one. Looking only at whether the final test cases pass makes these candidates appear more similar than they actually are.

Live interviews are valuable precisely because they expose the process that asynchronous assessments can obscure. Pay attention to how candidates decompose problems, what assumptions they make, how they react when an assumption turns out to be wrong, and whether they can explain why one solution is preferable to another.

This is also why interviewers shouldn’t rush to rescue candidates. A little friction is useful. In Predicting success, I argued that friction can generate evidence because it exposes the process behind a decision rather than simply its outcome. An interviewer who immediately provides hints whenever a candidate struggles destroys some of the most useful information the interview could have generated.

Of course, there’s a difference between productive friction and watching someone suffer for 20 minutes because they misunderstood a sentence in your instructions. Interviewers should clarify irrelevant confusion while avoiding assistance on the actual skill being evaluated.

You’re not trying to determine whether candidates never get stuck. You’re trying to learn what they do when they get stuck.

4. Decide when AI should enter the interview

The debate over whether candidates should use AI during interviews is often framed as a binary decision. That’s unnecessarily limiting.

There are skills worth evaluating without AI and others that increasingly make little sense to evaluate without it. The interesting opportunity is to test both.

Consider splitting an interview into phases. During the first phase, ask the candidate to reason through a problem independently. Once you’re comfortable that the underlying skill exists, introduce AI and see what happens to their performance. Do they provide useful context? Do they recognize incorrect output? Can they modify an approach rather than blindly accepting it? Does AI accelerate someone who already understands the problem or conceal someone who doesn’t?

Coderbyte lets organizations configure AI access differently for each interview. AI can be available from the beginning, disabled entirely, or controlled by the interviewer so that candidates only gain access when prompted. The integrated environment supports multiple GPT and Claude models, file context, and an agent mode that can make proposed changes to the codebase. In VS Code interviews, candidates can also work with Codex and Claude Code.

This enables a much richer interview than simply asking whether someone “knows how to use AI.” Give them a task without it, observe the baseline, then turn it on and observe the delta.

AI fluency isn’t demonstrated by using AI. It’s demonstrated by becoming meaningfully better with it.

5. Make the interview collaborative without making it easy

There’s a tendency for interviewers to fall into one of two extremes. Some behave like prosecutors, revealing as little as possible because any assistance might contaminate the evaluation. Others become overly collaborative and inadvertently solve half the problem themselves.

Neither produces particularly good evidence.

Real engineering is collaborative, so there’s nothing wrong with an interview being collaborative too. Candidates should be able to ask questions, challenge assumptions, discuss alternatives, and receive new information. The important distinction is whether the interviewer is adding realistic context or supplying the skill being measured.

If you’re evaluating requirements gathering, answer clarifying questions as a product manager would. If you’re evaluating debugging, provide information the candidate would realistically be able to obtain from logs or another engineer. If you’re evaluating system design, introduce changing requirements and discuss the consequences.

But don’t turn a struggling candidate into a successful candidate by progressively reducing the difficulty until they reach the answer.

The best technical interviews feel less like oral examinations and more like compressed working sessions. You’re getting a preview of what it might actually be like to solve problems with this person.

6. Standardize the questions, not the conversation

Structured interviews are valuable because candidates should be evaluated against comparable criteria. But companies sometimes interpret “structured” to mean that every interview must follow an identical script.

That removes one of the biggest advantages of having humans involved.

Standardize the competencies you evaluate, the core problems candidates encounter, and the scoring criteria interviewers use afterward. Then let the conversation evolve naturally enough to investigate interesting signals.

A candidate who makes an unusual architectural decision should be asked about it. Someone who finishes unusually quickly should receive a harder follow-up. Someone who identifies a flaw in the premise of the question should be allowed to pursue it rather than being redirected toward the expected answer.

Coderbyte allows teams to preload custom interview questions and templates while collecting candidate scorecards afterward. This combination is important: consistency in the evaluation framework without forcing every candidate through an unnaturally identical conversation.

Think of the interview as a controlled experiment. You want enough consistency to compare outcomes while preserving enough flexibility to learn something unexpected.

7. Don’t let interviewers grade their own personalities

Interview feedback has an uncomfortable tendency to describe the interviewer almost as much as the candidate.

One interviewer values speed. Another values communication. Another cares deeply about naming conventions. Another is impressed by clever solutions while their colleague distrusts them. Without explicit criteria, “strong technical candidate” can mean entirely different things depending on who happened to join the call.

This gets especially problematic when interviewers discuss candidates before independently recording their feedback. The first confident opinion becomes an anchor and everyone else begins evaluating their observations against it.

Before the interview, define what good performance looks like for each competency. Afterward, require interviewers to independently record evidence before discussing the candidate. Coderbyte’s interview environment supports private notes, candidate scorecards, playback, and interview analytics, which makes it easier to separate what actually happened from what an interviewer remembers happening.

Avoid feedback like “seemed senior” or “not sure they’re strong enough.” Instead, document the behavior that produced the conclusion. Perhaps the candidate identified a scalability problem without prompting, changed their approach after receiving a new constraint, or couldn’t explain why their implementation was failing.

The score matters less than the evidence behind it.

8. Build on previous stages instead of starting over

Hiring processes frequently suffer from organizational amnesia. A candidate submits a resume, completes an assessment, does a take-home project, and then arrives at an interview where someone asks them to solve an entirely unrelated problem from scratch.

Each stage generates information and then promptly forgets it.

This is particularly wasteful when a candidate has already completed substantive work. A take-home project is an excellent starting point for a live interview because the candidate understands the context and the interviewer already has evidence to investigate. Ask why they chose a particular architecture, change a requirement, introduce a bug, have them add a feature, or ask what they would do differently with another day.

Coderbyte supports loading saved questions and projects into interviews, including private GitHub repositories through VS Code projects. Instead of spending the first half of an interview establishing an artificial problem, you can immediately go deeper into something the candidate has already worked on.

Each stage of a hiring process should reduce uncertainty created by the previous stage. If you repeatedly test the same skill, you may become extremely confident about one dimension while learning almost nothing about the others.

9. Give candidates the tools they would have on the job

There’s a peculiar tradition in technical interviewing of deliberately making developers less productive.

Turn off autocomplete. Don’t let them search. Don’t let them use documentation. Give them an unfamiliar editor. Remove the packages they normally use. Then observe how they perform.

Some constraints are necessary when they protect the validity of what you’re measuring. Others are just inherited rituals.

If knowing an API from memory is irrelevant to job performance, let candidates look it up. If the team works in VS Code, consider letting them use VS Code. If developers routinely work with a terminal, packages, databases, and AI agents, removing all of them may make the interview more standardized while simultaneously making it less representative.

Coderbyte’s interview settings can be configured around the actual evaluation, including AI access, autocomplete, candidate lobbies, test-case visibility, IDE behavior, and other interview controls. Interviewers can also override global settings for an individual session when a particular exercise requires something different.

Constraints should exist because they improve measurement, not because technical interviews have always had constraints.

10. Record evidence while it still exists

Interview memory deteriorates surprisingly quickly. After several candidates, details blur together and interviewers begin remembering impressions rather than behaviors.

This is one reason hiring debriefs can become dominated by vague statements like “I liked her,” “he struggled a little,” or “she seemed stronger than the last person.” Those impressions aren’t necessarily wrong, but they’re difficult for anyone else to inspect.

Take private notes during the interview without turning the conversation into a transcription exercise. Complete scorecards immediately afterward. Where appropriate and with proper candidate consent, use interview playback to revisit moments that materially affect a decision. Coderbyte provides code interview playback along with reports, analytics, private notes, and scorecards so the hiring team doesn’t have to rely exclusively on memory.

This is particularly valuable for borderline decisions. Instead of debating whether a candidate “needed too much help,” you can identify which hints were given, when they were given, and what the candidate did with them.

Good hiring decisions depend on evidence. Preserve it.

11. Remember that the interview works both ways

Companies tend to design interviews as though candidates are the only party being evaluated.

Strong candidates rarely see it that way.

An interviewer who arrives late, hasn’t read the candidate’s background, struggles to explain the exercise, or spends an hour silently watching someone code is communicating quite a lot about the company. So is an interview environment that doesn’t work.

This doesn’t mean interviews need to be entertaining or easy. Candidates generally understand that a meaningful evaluation requires some difficulty. What frustrates people is pointless difficulty: broken environments, ambiguous expectations, irrelevant trivia, and interviewers who seem more interested in demonstrating their own intelligence than learning about the candidate.

Test your interview internally before using it. Make sure the expected scope fits comfortably within the available time. Give candidates a few minutes to become familiar with the environment and explain what resources they can use. Coderbyte can optionally disable its candidate lobby so candidates have a sandbox to practice in before the interviewer arrives, or enable the lobby when tighter control over the session is necessary.

The best candidate experience isn’t the easiest interview. It’s an interview where the difficulty feels relevant and intentional.

The rise of AI is forcing companies to reconsider technical interviews, but many of the underlying problems predate ChatGPT. Interviewers have always introduced bias. Artificial problems have always produced artificial signals. Candidates have always performed differently under observation than they do while actually working.

AI simply makes the shortcomings harder to ignore.

The opportunity isn’t to replace human interviews with AI or desperately preserve the coding interview of ten years ago. It’s to use live interviews for the things they’re uniquely good at measuring: reasoning, collaboration, adaptability, judgment, communication, and how someone responds when the problem changes underneath them.

A candidate’s ability to produce code matters. But code is increasingly cheap to produce. Knowing what code should be produced, recognizing when it’s wrong, and making good decisions about what to do next are becoming considerably more valuable.

Design the interview accordingly.