What do Karat technical interviews measure?

[This article was originally published in 2020, but has been updated to reflect the evolution of interviewing and what Karat technical interviews measure in the human + AI era.]

Going back to the early days of Karat, reviewing candidate feedback has been one of the most important ways we gauge the consistency, accuracy, and fairness of our interviews. Some of the feedback suggests Karat interviews are too hard. Others report that Karat interviews are too easy. As with any online forum, most of the extremes need to be taken with a grain of salt, but one overarching theme that has shown up over the years is that candidates think they’ll do poorly in technical interviews if they don’t solve questions fast enough.

Is that true?

Technical interviews measure more than coding speed. In the human + AI era, technical interviews assess problem-solving, engineering judgment, communication, systems thinking, and the ability to work effectively with AI tools. Modern technical interview rubrics increasingly evaluate how engineers validate AI-generated code, make technical tradeoffs, and apply foundational engineering skills in realistic workflows. 

Additional technical hiring resources

• AI interview rubrics
• Tech hiring trends report
• AI and the future of interviewing

Assessment and learning and technical interview evaluation

People are constantly learning. We start out as messy little balls who do nothing but eat, sleep, and poop. Eventually, we learn how to iterate through arrays and validate user input. Using that knowledge, people are constantly innovating and have even created AI systems that can do many of the tasks that we spent hours, days, or weeks on just a few years ago.

But people continue to learn. Each new piece of knowledge or skill that we learn builds on knowledge and skills that we’re already comfortable with. As people, it is natural to assess what we know as part of that learning cycle. We constantly and incrementally cycle between assessment and learning, expanding the foundation upon which new understandings and abilities grow.

2,500 years ago, Socrates asked questions as a teaching method in itself. We still do that today, albeit with new and improved granularity and color.

https://en.wikipedia.org/wiki/Bloom’s_taxonomy

What does speed actually measure in technical interviews?

Here are the dirty secrets, the lynchpins of learning and assessment, upon which all interviews are based:

• When a person is asked a question requiring knowledge or skills that they are very comfortable with, they can generally answer those questions correctly and quickly. If a candidate writes unit tests for breakfast without thinking, they can likely rattle off an explanation for “What is a unit test?” without blinking.

• On the other extreme, when a person is asked to explain or do something that they aren’t familiar with or don’t know how to do, they generally know right away that they don’t know. If a candidate has never heard of REST, their explanation to “What is REST?” is a short and sweet “I don’t know.” Similarly, if they’ve never used a hashmap and now need to use one, they would likely ask to look up syntax.

• The grey area is that tricky middle ground where someone is on the cusp of competency. This is where we need a sensitive and accurate signal. If a candidate understands the general concept of a depth-first search, they might slowly work out the implementation during the interview or get close before running out of time. If they have written and debugged a handful of programs, they might spend the whole interview working through error messages and logic bugs, sometimes getting a working solution in the nick of time and sometimes not.

So, interviews are a speed test?

Yes, in the sense that we can assess the coarse level of a specific, fine-grained competency by the correctness and speed of the answer.

But also, no, in the sense that counting minutes is neither a fine-grained nor linear scale. There might be a difference between solving a problem in ten minutes rather than 100 minutes, but a difference of two minutes is hardly meaningful.

Importantly, speed is a significantly less reliable indicator of competency in the human + AI era. AI tools can now generate correct answers or working solutions in seconds. Reaching the solution quickly isn’t necessarily a strong signal that the candidate has the underlying knowledge and skills needed — especially if it’s the product of a well-crafted (or lucky) prompt. This is a critical challenge facing employers. Traditional technical interview methods like coding tests and take-home projects no longer generate reliable hiring signals in the AI era.

The goal of any technical interview is to assess relevant and important competencies in a limited amount of time. At Karat, we work with hiring managers to define which competencies are relevant to success in a specific role and which are important to assess at a specific point in the hiring pipeline. Then, we identify the interview format and questions that will illuminate those competencies, which we call the signal, and measure them in a structured rubric.

These rubrics are getting more granular as AI makes it easier to obfuscate the underlying skills required to be a great engineer. We go into greater detail in Karat’s Human + AI rubrics resource, but the relationship between producing working code and deeply understanding how and why that code works (and where it might break) is weakening. Today, a working solution to a basic coding question can be produced with far less friction. And because of that, the most valuable engineering work in enterprise settings increasingly happens around the code, including:

• Determining where in a large codebase changes should be made
• Evaluating AI-generated suggestions for correctness and risk
• Making principled design tradeoffs under ambiguity
• Maintaining system integrity, performance, and maintainability over time

In the human + AI era, that signal includes more than traditional coding fundamentals. Engineers also need strong communication, problem-solving, systems design, and technical decision-making skills. Companies are increasingly evaluating whether candidates are AI-ready and capable of working effectively in AI-assisted software development environments.

Modern technical interviews also assess how candidates collaborate with AI systems while maintaining engineering quality, judgment, and accountability.

Additionally, engineering leaders are looking for candidates to have new AI-native abilities:

• Familiarity with agentic AI
• Using AI for coding
• Integrating third-party AI APIs
• Prompt engineering
• Evaluating and mitigating AI-related risks

There are a few conclusions that follow from this setup.

Why one technical interview format does not fit all

There isn’t one Karat interview. Each Karat interview is tied to a specific role at a specific company, and each candidate’s performance is evaluated by that specific company based on its specific needs. [Note: our Interview Engineers do their best to highlight a candidate’s strengths, the Karat Interviewing Infrastructure uses their observations to make a recommendation, and the hiring company makes the final decision.]

What questions candidates get, how many they are asked, and what skills they have to demonstrate to move forward, depend on the company and role. We tailor interview experiences to different types of engineering work, including full-time roles and contractors across the world. 

When candidates schedule an interview, Karat sends a personalized email explaining what they should expect and how to prepare. If the candidate has additional questions, they can reach out to Karat’s customer experience team, as well as the hiring company’s recruiter.

What is the hiring signal in a technical interview?

While speed can reflect familiarity and fluency, speed alone is not the hiring signal in modern technical interviews. Doing well in an interview is not meant to feel like winning a race, so much as working through coding problems in a steady, straightforward way without getting stumped or lost for long stretches of time.

In the AI era, foundational engineering skills matter, but that’s not all. Karat NextGen interviews still surface whether a candidate can apply core concepts, but we also focus on how those skills show up in human + AI workflows.

Modern technical interviews commonly evaluate:

• Problem-solving ability
• Coding fundamentals
• Technical communication
• Systems thinking
• Debugging and validation
• Engineering judgment
• AI collaboration skills
• Risk evaluation and tradeoff analysis

Here’s how some of these skills often appear in engineering workflows:

• Problem-solving and judgment: Clarifying assumptions, verifying uncertainties, and managing risk before implementing a solution.
• Abstraction and systems thinking:  Choosing appropriate data structures or interfaces, and breaking problems into manageable components that can evolve.
• Coding fundamentals: Implementing clear code that neither hides bugs nor gets in the way of debugging.

Technical communication: Explaining tradeoffs, what’s causing a bug, and their decision-making process.

• AI collaboration skills: Using AI tools effectively through prompting, reviewing AI output, recognizing when AI is wrong or incomplete, and integrating AI-generated code.

Why technical interviews are an engineering trade-off

Filtering candidates based on resumes and code tests can introduce noise. This is especially true for false negatives, rejecting candidates even though they could be great hires.

On the other hand, if every candidate requires hours of interviews with multiple engineers, engineers may find themselves working overtime to keep up, while the engineering team as a whole misses both product deadlines and hiring goals.

Many companies rely on IT Service Providers to save time and move faster when hiring. However, contractor engineering quality is often inconsistent and below the talent bar. 

We’re engineers. We’re used to trade-offs! One-hour live interviews strike a useful balance because Interview Engineers deliver both improved candidate experience and a stronger hiring signal by assessing interpersonal and decision-making skills that solitary tests cannot, and by maximizing candidate performance to reduce false negatives, such as clarifying silly misunderstandings and providing encouragement during a nerve-wracking brain freeze.

Additionally, live interviews that involve a human interviewer and AI tools produce better outcomes. Our data shows that over the next three years:

• 63% of companies that use human + AI interviews expect coding errors to decrease.
• 49% of companies that use human + AI interviews expect the time it takes to bring new products/features to market to decrease.
• 76% of companies that use human + AI interviews expect the number of products and features they release to increase.  

We extensively test Karat interview questions to calibrate expectations, ensure they aren’t overly sensitive (overfit), and reduce noise. The goal is that candidate performance demonstrates a true signal on the competencies specifically relevant and important for that particular hiring process.

What does this mean for candidates?

We design Karat interviews to provide some challenges, so just because someone isn’t breezing through an interview doesn’t mean they are failing. Maybe grappling with difficult questions is successful behavior. We encourage everyone to focus on doing their best, be positive, and let the hiring company make the decision.

I hope you found this post insightful. Big thank you to all the candidates who leave us feedback after their Karat interviews. Our interviews are a solution to the problem of assessing technical talent, however, we’re always working hard to find a more optimal solution — more predictive, more fair, more enjoyable. Keep leaving feedback, and we’ll keep you updated with what we learn.

Frequently asked questions about technical interviews

Are technical interviews just speed tests?

No. While speed can reflect familiarity with technical concepts, modern technical interviews primarily assess problem-solving, engineering judgment, communication, and technical decision-making.

What do technical interviews measure in the AI era?

In the human + AI era, Karat interviews measure both foundational engineering skills and AI-native abilities, including validating AI-generated code, prompt engineering, and evaluating technical risk.

Why do companies still use technical interviews if AI can generate code?

Companies still use technical interviews because engineering success depends on more than producing code. Employers need engineers who can evaluate correctness, maintain systems, communicate decisions, and make sound tradeoffs in complex environments.

What skills are most important in modern engineering interviews?

Modern engineering interviews commonly evaluate problem-solving, debugging, systems design, communication, engineering judgment, and AI collaboration skills.