The expert guide for running technical assessments

Technical hiring has an AI problem. Actually, it has two.

The obvious problem is that candidates can now use AI to complete work they previously had to do themselves. The less obvious problem is that employers are responding by trying to recreate a world that no longer exists. Developers use AI at work, and increasingly they’re expected to. According to HackerRank’s 2025 Developer Skills Report, 97% of developers use AI assistants and nearly one-third of code is AI-generated on average.

So banning AI everywhere during a hiring process is increasingly detached from the actual job. But allowing it everywhere is equally useless. If a candidate can delegate every meaningful task to an agent, you haven’t evaluated the candidate—you’ve evaluated the agent.

The goal of technical hiring is therefore no longer to create an artificial environment where AI doesn’t exist. It’s to deliberately separate the skills candidates should possess independently from the skills they should demonstrate with AI.

That requires thinking beyond the traditional coding test.

Coderbyte has evolved in the same direction. What started primarily as a technical assessment platform now spans much more of the hiring funnel, including resume screening, application fraud detection, agentic phone screens, coding assessments, take-home projects, AI-enabled development environments, and live interviews. The technology has changed considerably, but the principles of good evaluation haven’t.

Here are the ones that matter.

1. Test the work, not preparation for the test

For years, technical assessment became nearly synonymous with algorithmic coding challenges. That made sense when the primary question was simply, “Can this person code?” It makes less sense when the actual job involves navigating an existing codebase, debugging, reading documentation, reviewing AI-generated code, writing SQL, manipulating data, communicating tradeoffs, and deciding whether an agent’s output is actually correct.

There is nothing inherently wrong with algorithmic challenges. They are useful when the underlying skill is relevant to the role. The mistake is confusing something that is easy to grade with something that is important to know.

A backend engineer probably shouldn’t be evaluated exclusively on reversing binary trees, just as a data analyst shouldn’t be evaluated exclusively with multiple-choice questions.

Start with the actual job. What does a high-performing person in this role repeatedly need to do? Then build the evaluation backward from those activities.

With Coderbyte, that can mean combining algorithmic or multi-file coding challenges with take-home projects, spreadsheet challenges, free-form questions, video responses, whiteboards, or your own custom material. The platform includes more than 10,000 questions across a broad range of skills, languages, and technologies, but the larger opportunity is using different assessment formats to more closely approximate the work itself.

Your assessment should feel like a compressed version of the job, not a separate academic discipline candidates must study in order to get the job.

2. Decide where AI is a tool and where it is a crutch

“Should candidates be allowed to use ChatGPT?” is becoming the wrong question. The better question is: For which skills should candidates be allowed to use AI?

There are still foundational abilities worth testing independently. A developer should be able to reason through code, a data analyst should understand the query they are running, and a security engineer should recognize a dangerous implementation. Giving an AI agent unlimited access during every stage can obscure whether those foundations actually exist.

But there are also increasingly important skills that can only be evaluated by allowing AI. Can the candidate write a useful prompt, give an agent enough context, recognize when the output is wrong, improve mediocre generated code instead of blindly accepting it, and explain what they kept, rejected, and changed?

These are becoming job skills.

Coderbyte therefore supports both approaches. During conventional assessments, employers can detect suspicious AI usage. For projects where AI use is intentional, candidates can instead work in an AI-enabled environment while employers evaluate both the final output and how the candidate arrived there. Coderbyte’s development environments can also expose tools such as Codex and Claude Code when agentic development is itself part of the job.

The important thing is to be deliberate. If you prohibit AI, enforce the prohibition. If you allow AI, evaluate how it is used. The worst option is pretending candidates aren’t using it while having no visibility into whether they are.

3. Measure AI fluency, not AI enthusiasm

Almost everyone can open ChatGPT. That doesn’t make everyone good at working with AI.

In fact, the more powerful the models become, the easier it is to confuse access with ability. The differentiator is increasingly judgment: knowing how to frame a problem, how much context to provide, when to intervene, and whether an answer that looks plausible is actually correct.

That tension is already showing up in developer behavior. Stack Overflow’s 2025 survey found that AI adoption continues to grow even while more developers distrust AI accuracy than trust it. This apparent contradiction is actually the job. Good developers use powerful tools without surrendering responsibility to them.

For technical roles, one of the most effective ways to evaluate AI fluency is therefore to give candidates a sufficiently complex project, allow AI assistance, and evaluate both the outcome and the process. Coderbyte’s AI-fluency workflows can provide visibility into how candidates prompt an agent and work through the resulting implementation.

You’re not trying to determine who can generate the most code. You’re trying to determine who knows what good code looks like after it has been generated.

4. Protect integrity without turning the assessment into airport security

AI didn’t invent cheating. It simply made cheating faster, cheaper, and harder to notice.

Hiring teams are understandably reacting with increasingly aggressive controls. But there is a point where protecting assessment integrity starts degrading the candidate experience enough that strong candidates opt out. The objective isn’t maximum surveillance. It’s sufficient confidence.

Coderbyte supports multiple layers of prevention and detection, including challenge masking, randomized questions, identity verification, screenshot controls, screen recording, suspicious browser activity monitoring, AI-usage detection, and webcam proctoring. Its suspicious activity monitoring can also flag situations such as multiple people being visible or another device appearing during an assessment.

The right combination depends on the stakes. A short initial screen probably doesn’t need the same controls as a final-stage assessment for a privileged security role.

Use enough friction to establish trust, but remember that friction cuts both ways. You are evaluating the candidate, and the candidate is evaluating you.

5. Screen broadly, then spend human time narrowly

One of the stranger things about recruiting is that companies routinely acknowledge that resumes are imperfect predictors of job performance and then use resumes to determine who gets an opportunity to demonstrate job performance.

That creates a bad funnel. A recruiter spends substantial time manually reviewing applications, applies imperfect proxies to reduce the pile, and then asks a relatively small percentage of candidates to demonstrate the thing the company actually cares about.

AI makes this problem both worse and easier to solve. Candidates can now generate and customize applications at extraordinary scale, while employers can automate more of the initial screening process. Taken too far, this becomes two machines trying to impress each other while the humans arrive increasingly late.

Automation should instead expand signal, not replace judgment.

Coderbyte’s screening tools can evaluate resumes against job-specific criteria and conduct agentic phone screens based on the role and candidate background. Phone-screen reports can then include the recording, transcript, evaluation criteria, supporting evidence, confidence scores, and an AI-generated analysis.

That can substantially reduce repetitive screening work, but the advantage isn’t that AI can make the hiring decision for you. It’s that your team can spend more of its limited time investigating candidates who deserve deeper consideration.

6. Optimize for completion, not comprehensiveness

Hiring teams love adding questions. One more coding challenge, one more multiple-choice section, one more behavioral response, one more “quick” exercise. Each addition feels inexpensive because the employer isn’t the one taking the assessment.

But every minute has a cost.

The strongest candidates often have the most alternatives. A three-hour assessment therefore doesn’t necessarily filter for the most qualified candidate. Sometimes it filters for the candidate most willing to spend three hours on your assessment.

Your goal should be to collect the minimum amount of evidence required to make the next decision. For most early- and middle-stage assessments, that means concentrating on the few skills that are actually predictive of success rather than trying to evaluate everything someone might conceivably need on the job.

Coderbyte lets teams weight sections differently and configure qualifying scores so that the most important signals can matter more than a long list of low-value questions. More evidence is not automatically better evidence. Sometimes it’s just a longer test.

7. Use AI to review more evidence, not to manufacture certainty

Automated grading has always been attractive because it turns a complicated decision into a number. A score of 92 feels more authoritative than “this candidate seems pretty strong,” even when the underlying measurement is imperfect.

AI now makes it possible to analyze much richer candidate work than deterministic test cases alone. Coderbyte can provide AI-powered analysis of candidate submissions across dimensions such as accuracy, efficiency, documentation, and best practices. Take-home work can also be reviewed against employer-defined rubrics.

That’s useful because it increases the amount of evidence a reviewer can reasonably process. It shouldn’t eliminate the reviewer.

Coderbyte itself recommends that domain experts review candidate submissions before making hiring decisions. AI should make evaluation less tedious, not accountability less clear.

8. Tell candidates what game they are playing

Many bad candidate experiences aren’t caused by difficult assessments. They’re caused by ambiguity.

Candidates don’t know how long something will take, whether Google is allowed, whether AI is allowed, what monitoring is enabled, or what happens after they finish. Sometimes they don’t even know whether anyone will look at the result. The employer has all of this context while the candidate has almost none.

Before an assessment begins, tell candidates what skills are being evaluated, approximately how long it should take, which resources they may use, whether AI is permitted, what monitoring is enabled, and what happens next. If the environment itself could be unfamiliar, let candidates practice first.

Coderbyte supports customizable invitation emails, welcome-screen instructions, public practice assessments, custom branding, and other candidate-facing assessment controls that can remove unnecessary uncertainty before an evaluation starts.

Hard is fine. Confusing is not.

9. Connect the stages instead of creating separate auditions

A common hiring process looks something like this: resume screen, phone screen, technical assessment, technical interview, final interview. Each stage is run by a different person, often in a different tool and with a different set of questions. By the end, the company has accumulated a lot of activity but surprisingly little continuity.

A better process compounds evidence.

If a candidate completes an interesting take-home project, use it during the live interview. Ask why they made certain architectural decisions, have them extend their implementation, introduce a bug, or ask them to improve something the AI generated. Coderbyte lets teams carry take-home projects into subsequent evaluation stages so the next conversation can build on work the candidate already completed rather than starting over.

The same principle applies operationally. Assessment invitations and results should flow through your recruiting systems rather than requiring recruiters to manually ferry information between them. Coderbyte supports integrations with common ATS platforms and additional workflows through automation tools.

Every stage should create information that makes the next stage better. Otherwise you are repeatedly meeting the same candidate for the first time.

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Technical hiring used to revolve around a relatively simple question: can this person do the work?

AI has made the question more complicated and, in many ways, more useful. Now you need to understand whether candidates possess the underlying skills, whether they can multiply those skills with AI, whether they can recognize when AI fails, and whether the evidence you collected throughout the process actually belongs to them.

Trying to answer all of that with one coding test is like trying to evaluate a chef by asking them to chop an onion. It’s useful signal, but a very incomplete meal.

The best hiring processes therefore won’t be the ones that most aggressively prohibit AI or the ones that most enthusiastically automate everything. They’ll be the ones that deliberately decide what should remain human, what should be augmented, and what can safely be automated—and then test accordingly.