Introduction
In a practical coding interview, you might receive an unfamiliar codebase and a feature request, a broken project to debug, or a take-home assignment to build and present. These rounds test how you handle work that looks more like engineering than an isolated algorithm exercise.
AI-first startups, along with a growing number of large technology companies, expect engineers to know how to work with coding agents and AI tools. We call these practical coding rounds. They include live projects, take-homes, debugging exercises, and code reviews.
You still need to write correct code, but that is only part of the work. You'll also need to understand unfamiliar systems, make decisions when the specification has gaps, validate what you build, and use AI without handing over your judgment.
What counts as a practical coding interview?
"Practical coding" covers several interview formats. Each one simulates real engineering work rather than testing an isolated algorithm:
- Multi-file live interviews. You work on a realistic project, not a single function. Usually 45 to 90 minutes, in a shared environment like CoderPad or your own editor over a screenshare.
- Take-home projects. Build something from a spec on your own time, then often walk through it in a follow-up round.
- Debugging rounds. Reproduce and fix bugs in code you've never seen, or diagnose a misbehaving system interactively with your interviewer.
- Work samples and presentations. Some companies have you extend a real repo on-site, or present your take-home to a panel and defend your decisions.
If the main challenge is finding an algorithmic insight, it belongs in our Coding Interview course. If the challenge is working through a realistic engineering environment, it belongs here.
How do they differ from DS&A questions?
A classic algorithm question gives you an input and expected output, then asks for an efficient solution. Practical questions are messier. The specification may have gaps, the data may contain unmentioned edge cases, and you may need to research a library or decipher a codebase before you can start.
Interviewers still care about communication, problem solving, and coding ability. Practical formats also reveal how you research unfamiliar topics, whether your code can accommodate a new requirement, and whether you can use an AI agent productively.
How this course is organized
Every practical interview sits somewhere on a spectrum between reading and writing code. At one end, you start with little more than a specification and build a working application. At the other, you spend most of the round reading a complete codebase to find one bug.
We'll cover that spectrum in three modules:
- Building new projects from scratch. Creating something real from a spec.
- Extending existing codebases. Understand an existing codebase and build on it productively.
- Debugging. Investigate, reproduce, and fix bugs in existing codebases and systems.
In each module, we include mock interviews and downloadable codebases to practice with.
The course includes practice problems modeled on reported interviews, including this one from the end of the spectrum where you write most of the code:
The AI-usage spectrum
Companies take different approaches to AI during interviews. Before you practice, it helps to understand the three common levels of access:
- Research-only. Use AI like a better search engine: look up an API, compare libraries, or check syntax. You write the core logic yourself.
- Limited context. AI can help with parts of the problem, but you drive the design and you verify everything it produces.
- Full use. You're expected to use an agent to execute. The signal becomes your judgment: what parts do you delegate, how you review the output, and how you structure prompts.
Whatever the format, clarify before you build, explain your reasoning, and validate each step, including anything AI produces.
Practice the modules in order if these formats are new to you. If you already know your target company uses one format, feel free to jump to that module.