Karat NextGen Explained: The Multi-File, AI-Enabled Interview Format (2026)
Details in this article reflect what is known as of mid-2026. NextGen is an evolving format and specifics may change.
LeapFork is an independent interview preparation platform, not affiliated with Karat, Inc. This guide covers what is publicly known about Karat NextGen based on announcements and candidate reports.
Technical interviews are changing. For years, the standard Karat format asked candidates to solve a single algorithmic problem from scratch in 40 minutes. That format was valuable, but it did not reflect how most engineers actually work — which involves reading existing code, navigating a codebase, and increasingly, directing AI-generated code. Karat NextGen is a response to that gap.
What Karat NextGen Is
Karat NextGen replaces the single blank-file coding problem with a multi-file codebase in an AI-enabled IDE. Instead of writing a solution from scratch, candidates work within an existing codebase — navigating files, understanding structure, making targeted improvements, and evaluating AI-generated code suggestions.
As of mid-2026, NextGen is being deployed by select Karat client companies. It is not yet the universal Karat standard — the classic single-problem format remains in use at many hiring companies. Whether you face NextGen or the classic format depends on the specific company you are interviewing with.
How NextGen Differs from the Classic Format
The contrast is significant enough that preparation for one does not fully transfer to the other:
| Dimension | Classic Karat | Karat NextGen |
|---|---|---|
| Starting point | Blank file | Multi-file existing codebase |
| Core task | Implement a solution to an algorithmic problem | Navigate, understand, and improve existing code |
| AI tools | Not present | AI code assistance integrated in the IDE |
| Primary skill assessed | Algorithm fluency, problem decomposition | Codebase reading, refactoring judgment, AI direction |
| Communication expectation | Narrate your approach as you code | Explain your reasoning about existing code and your changes |
In the classic format, you demonstrate you can build something. In NextGen, you demonstrate you can operate effectively within something — and make smart decisions about AI assistance along the way.
The New Skills NextGen Tests
Codebase Navigation
Before you can improve code you do not understand, you must be able to read and navigate it quickly. NextGen tests whether you can identify the relevant sections of a multi-file codebase, understand the existing architecture without needing it explained, and locate the right place to make a change. This is a genuine skill that improves with practice on real codebases.
Refactoring for Maintainability
NextGen tasks are likely to include making changes that are correct but also clean — reducing duplication, improving readability, or making a function easier to test. This is not just about the code working; it is about whether an experienced engineer would approve the change. Rubric dimensions in NextGen are expected to include code quality in a way the classic format does not emphasize.
Evaluating and Directing AI-Generated Code
The AI-enabled IDE in NextGen presents candidates with AI-generated code suggestions. The interview is partially evaluating whether you can:
- Recognize when an AI suggestion is correct, incorrect, or incomplete.
- Accept, reject, or modify suggestions with clear reasoning.
- Avoid blindly accepting AI output without review (a known failure mode).
This is a skill that benefits everyone who works with AI coding tools regularly. Candidates who use GitHub Copilot or similar tools daily in their work are likely to find this dimension more natural than those who have not used AI coding assistance before.
What NextGen Signals About Where Technical Screens Are Heading
Karat NextGen reflects a broader shift in how the industry thinks about measuring engineering capability. The blank-slate algorithmic problem has always been a partial proxy — it captures some skills (algorithm fluency, problem decomposition) while missing others (code reading, judgment, collaboration style).
The addition of multi-file context and AI tool integration moves the format closer to actual engineering work. A candidate who can navigate an unfamiliar codebase, make a targeted improvement, and critically evaluate an AI suggestion is demonstrating skills that are directly relevant to a modern software engineering role.
The trajectory suggests that over the next few years, more companies will adopt NextGen-style formats. Candidates who develop codebase-reading and AI-evaluation skills now are building durable interview preparation, not just preparation for a single company's screen.
How to Practice for Karat NextGen
Preparing for NextGen is different from classic Karat prep. Here is what works:
- Practice reading unfamiliar codebases: Open a mid-sized open source project (a few hundred to a few thousand lines) in your language and navigate it without any readme guidance. Identify the entry point, understand the data flow, and find where specific logic lives. Time yourself.
- Practice refactoring exercises: Take working but messy code and improve it — without breaking functionality. Focus on: reducing duplication, improving variable names, extracting functions, and making tests easier to write.
- Build AI tool judgment: Use GitHub Copilot, Claude, or GPT-4 to generate code for small problems you already know. Deliberately review every suggestion before accepting it. Note where the AI is wrong, incomplete, or subtly off. This builds the critical review reflex.
- Narrate your code-reading: Just as the classic format requires narrating your algorithm, NextGen requires narrating your understanding of the existing code. Practice doing this out loud: "This function takes a list and returns a filtered version. It seems to be doing X. I would want to change Y because Z."
LeapFork's timed simulation mirrors the actual Karat format: live coding, AI-graded performance, and a speaking AI interviewer. Free to start.
Run a Free Simulation →Frequently Asked Questions
As of mid-2026, NextGen is being rolled out to select client companies. The classic single-problem coding format remains in use at many companies. Which format you will face depends on the specific hiring company's Karat contract. Ask your recruiter if you are unsure.
It tests different skills rather than being definitively harder. Classic Karat requires fluency with a focused algorithm problem from scratch. NextGen requires codebase navigation, refactoring judgment, and the ability to evaluate AI-generated code — skills that experienced engineers may find more natural.
Candidates work in a multi-file codebase within an AI-enabled IDE environment. The codebase is purpose-built for the interview — it is not a real production codebase. It is large enough to require navigation but scoped to a manageable problem domain.
The "AI-enabled IDE" in NextGen means the environment includes AI code assistance features. The interview evaluates your ability to work with, direct, and critically evaluate AI suggestions — not just write code from scratch. This is explicitly part of the skill being assessed.
Ask your recruiter directly: "Will my Karat interview use the standard format or the NextGen multi-file format?" Recruiters should have this information. If they do not, ask them to confirm with the hiring team before your scheduled session.
LeapFork is an independent platform building toward NextGen preparation. Our current simulation mirrors the classic Karat format with a speaking AI interviewer and AI grading. NextGen-specific practice (multi-file codebase navigation) is on our roadmap.