AI coding interview questions: real tasks from 150 candidate reports
AI-assisted coding interviews rarely ask a single LeetCode puzzle. We went through 150 candidate reports from 38 companies to see what the questions actually are. Here are the six kinds of task that come up, each with real examples and how to approach it.
The numbers
Of the 150 reports in our database, 111 describe the task:
| Task | Reports |
|---|---|
| Fix bugs in an existing codebase | 50 |
| Build something from a spec | 36 |
| A mix of the above, in stages | 17 |
| Extend existing code | 8 |
Most rounds last about an hour: 60 minutes is by far the most common length in reports that give one. Bug fixing is common partly because Amazon’s online assessment, the biggest group in our reports, is built around it. Each report is a summary of a public post or an official page, and each is linked to its source.
1. Make the failing tests pass
You get a repo you’ve never seen, some failing tests and an AI assistant. Real examples:
- Amazon OA: pick a framework (Django, Spring Boot, C++ or Node.js), then get 6 failing unit tests to pass in 60 minutes (LeetCode Discuss). Another candidate’s repo was a wallet app with backend API bugs in paths, URLs and names. More in our Amazon OA guide.
- Capital One: a codebase of about 30 files and one instruction, here’s a bug, fix it (r/leetcode).
- Block: download a Kotlin codebase and track down an issue with a coding agent (Blind).
Approach: run the tests first, read the failure, and ask the AI to explain the code path before you ask it for a fix. Check every fix against the test that failed.
2. Build a small system from a spec
A short problem statement, and you design and build it, often with object-oriented code and simple algorithms:
- Rippling: an expense policy engine that applies configurable rules to card transactions and returns approved or rejected with a reason (LeetCode Discuss). See our Rippling guide.
- Atlassian: a configurable logging system from a three-line problem statement, so you scope the requirements yourself.
- Snap: design a data schema, an API and the business logic (Blind).
- Microsoft: a low-level design problem, written as comments and pseudocode before the AI writes code. See our Microsoft guide.
Approach: spend the first few minutes on design: name the entities and the rules, agree them with the interviewer, then let the AI scaffold while you write the core logic.
3. Extend an existing codebase
- Atlassian: add validation rules to a PATCH endpoint that updates a card’s PIN, in a Java Spring Boot app. The catch was in the data model (LeetCode Discuss).
- DoorDash: a workflow engine for automating support tickets such as late orders: make the test pass, then extend it (LeetCode Discuss).
Approach: find where the change belongs and read the existing tests before writing anything. Ask the AI where similar logic already lives.
4. A classic problem with extensions
Some companies keep algorithm questions and add parts:
- LinkedIn: an LRU-cache-style problem with 3 or 4 extensions. You must come up with the core logic yourself; asking the AI for the algorithm doesn’t work (Blind). See our LinkedIn guide.
- Google: two LeetCode problems, a medium and a hard, with pseudocode required before touching the AI (r/leetcode).
- TikTok: problems such as expression calculators, course scheduling and word-ladder-style BFS (Blind).
Approach: keep your algorithm skills sharp. Use the AI for boilerplate, tests and edge cases, not to choose the approach.
5. Multi-stage: fix, build, then scale
Meta’s AI-enabled round is the clearest example. One candidate’s card game task: draw cards into a grid and pick 3 whose values sum to 15. First fix a failing test, then implement a strategy that keeps picking valid triples (Blind). Others describe a maze solver with stages that scale up and need optimising. See our Meta guide.
Approach: keep each stage’s code clean, because the next stage builds on it, and watch complexity before the scale-up arrives.
6. Requirements first, then code
- Ramp: the interviewer plays the product manager and won’t answer implementation questions. You agree a minimum spec, then build it with a coding agent (r/InterviewDB).
- Instacart: a CodeSignal assessment starting with a chat with an AI product manager to gather requirements for a search and filter feature (r/InterviewDB).
Approach: ask about edge cases, limits and what “done” means before you write a line. Write the agreed spec down where the AI can see it.
Code review rounds are a seventh, newer kind: you review AI-written changes instead of writing code. See our code review interview guide.
Practise these
- Our free practice projects combine kinds 1, 3 and 5: an unfamiliar Java codebase, a bug, a feature and a speed-up, each with tests.
- Company by company: 150 candidate reports, and which companies allow AI.
Sources
All numbers come from our candidate report database as of 4 November 2026. Examples above link to the original posts.