How to Prep for an AI-Allowed Coding Interview: They're Grading Your Prompts

Last updated: September 2026.

Quick answer: In an AI-allowed coding interview, the tool is permitted and the scoring expands. You are graded on how you decompose the problem, how you construct your prompts, whether you can verify what the model returns, and whether you can explain and defend the code. The solution matters; owning the solution matters more.

When a company permits AI tools in a coding round, the rubric expands rather than shrinks. A candidate who pastes the full problem statement into a chat window and copies the first response into the editor has demonstrated nothing a hiring team wants to measure. The interviewers are watching how you think before the model runs, what you do with what comes back, and whether you could maintain this code months from now.

That is what the rest of this guide covers.

What Do Interviewers Actually Grade?

The rubric in an AI-allowed round includes signals that a traditional interview largely skips:

Verification and explanation are the two dimensions the rubric weights most heavily. Both are trainable.

How Does an AI-Allowed Round Actually Run?

Most AI-allowed rounds give you access to a coding environment with an AI tool built in — either Copilot in the IDE or a chat panel you can query. The session runs like a standard coding round: one interviewer, one problem, 35 to 45 minutes.

The visible difference is in the debrief criteria. Interviewers in these rounds often score effective AI use as a separate dimension alongside problem-solving and communication. A candidate who produces working code but cannot explain it, or who accepted incorrect output without checking it, scores low on that dimension regardless of whether the function runs.

Two things to expect after you present: a request to walk through the solution in detail, and a follow-up like "what would you change if the input size grew by 100x?" — a question where the model cannot help you if you do not understand the algorithm underneath.

What Makes a Good Interview Prompt?

A prompt that returns usable interview output has four parts:

1. Goal — what the function should do, in plain English
2. Signature — input type, output type, and any type constraints
3. Performance target — the time or space complexity you are aiming for
4. Edge cases — the specific inputs you already know should produce a particular result

The difference in practice:

• Weak: "Two sum problem"
• Stronger: "Write a function that takes an array of integers and a target integer and returns all unique pairs that sum to the target. It should run in O(n) time and return an empty list if the input array is empty."

The stronger prompt takes an extra ten seconds to write and returns something calibrated to the actual spec. The weak one returns the standard version, which may solve a different variant than what you were asked.

How Do You Work Through a Problem?

Work the round in five stages. The order is the point.

Stage 1: Understand before you prompt. Read the full problem statement. Write down the input type and output type. Note the performance constraint and at least two edge cases. Do not prompt yet. This step forces you to understand the question rather than handing that work to the model.

Stage 2: State your approach in plain language. In a comment block or scratch pad, write your algorithm in two to four sentences. What data structure are you using? What time complexity are you targeting? What happens on empty input? This becomes both your prompt and your explanation.

Stage 3: Prompt with all four parts. Turn your plain-language approach into a prompt covering goal, signature, performance target, and edge cases. Prompt once and read the output before running it.

Stage 4: Verify before presenting. Trace through the AI's output on your two edge cases. Check: does it compile? Does it handle empty input? Does it return the right type? Fix anything that does not match. This is the stage most candidates skip, and it is where most interview failures happen.

Stage 5: Explain and defend. Walk the interviewer through the solution. Name the data structure, state the time and space complexity, and be ready to modify a section under time pressure. If you cannot do this fluently, Stage 4 did not go far enough.

Which Problems Should You Practice?

The ten problems below are restated from widely shared interview practice, with original solution approaches. They are illustrative examples, not questions from any specific company.

1. Array pair sum Given an unsorted array of integers and a target integer, return all unique pairs that sum to the target. Aim for O(n) time. Suggested approach: One pass with a hash set. For each element, check if target - element is in the set. If yes, record the pair. If no, add the element.

2. String compression Given a string like "aaabbbcc", return the compressed form "a3b3c2". If the compressed string is not shorter than the original, return the original. Suggested approach: One pass tracking the current character and a running count. Build the result string incrementally and compare lengths before returning.

3. Valid balanced brackets Given a string containing only parentheses and curly and square brackets, return true if every opening bracket closes in the correct order. Suggested approach: Stack. Push opening brackets. On a closing bracket, check whether the top of the stack is the matching opener. Return false if the stack is non-empty at the end.

4. Level-order tree traversal Given a binary tree, return the node values level by level, each level as a separate list. Suggested approach: BFS with a queue. At each level, record the queue's current length, process that many nodes, and enqueue their children.

5. Longest increasing subsequence Given an array of integers, return the length of the longest strictly increasing subsequence. Suggested approach: Dynamic programming. For each index, the LIS length ending there equals 1 plus the maximum LIS length at any earlier index with a smaller value. A binary search variant achieves O(n log n).

6. Island count Given an m×n grid of '1' (land) and '0' (water), count the number of islands. An island is a contiguous group of '1' cells connected horizontally or vertically. Suggested approach: DFS from each unvisited '1'. Mark visited cells to avoid counting the same island twice. Increment the counter each time you start a new DFS.

7. Minimum path sum Given an m×n grid of non-negative integers, find the path from top-left to bottom-right that minimizes the total sum. You can only move right or down. Suggested approach: DP in place. Each cell holds the minimum cost to reach it: min(cell above, cell to the left) + current value. The answer is in the bottom-right cell.

8. First non-repeating character Given a string, return the index of the first character that appears exactly once. Return -1 if none exists. Suggested approach: Two passes. First: count each character with a hash map. Second: return the index of the first character with count 1.

9. Merge intervals Given a list of intervals as [start, end] pairs, merge all overlapping intervals and return the result. Suggested approach: Sort by start value. Iterate through, extending the last merged interval if the current start overlaps it, or appending a new one otherwise.

10. LRU cache Implement a cache with get(key) and put(key, value) that both run in O(1). When the cache is at capacity, evict the least recently used item. Suggested approach: A doubly linked list with the most-recently-used item at the head, combined with a hash map from key to list node. get moves the node to the head. put inserts at the head and removes the tail when over capacity.

Practice each problem by writing your plain-language approach before prompting, then prompting with all four parts, tracing through the output on two edge cases, and explaining the solution out loud. That loop is the actual skill the interview tests.

Try the free sample problems at PokeBot's SWE workbook →

Where Do Most Candidates Lose Points?

Three failure patterns are worth knowing before you sit down.

Pasting without reading. You prompt, get a response, and paste it into the editor without reading it. The interviewer watches you present a solution with a bug you could have caught in thirty seconds. Read the output before you paste it, every time.

No edge-case verification. AI output solves the happy path cleanly. An empty input or a single-element array — these are where the first draft often fails. Name two edge cases before prompting and trace through them before presenting.

Prompting the interviewer's exact words. The problem statement is often deliberately underspecified. Handing the ambiguity to the model rather than resolving it yourself means you lose the decomposition credit. Resolve the ambiguity first, then prompt. That resolution is part of what the interviewer came to see.

How Do You Build This Skill Before the Interview?

Normal coding practice with AI tools builds a different habit than AI-allowed interview prep. In practice, you keep iterating until the code runs. In the interview, you present once and then explain immediately.

After solving any practice problem with AI assistance, close the editor and explain the solution out loud from memory. Name the data structure. State the time and space complexity. Modify one section under artificial pressure — set a 90-second timer. If you cannot do this, you can run the code but you do not own it yet.

The same principle applies as in in-person, no-AI prep: delivery and comprehension are built by doing them out loud, repeatedly. An AI-allowed round is harder to own because the code came from somewhere else, and the follow-up questions are designed to surface that gap.

For context on where the line falls when AI is not explicitly permitted, Is Using AI in an Interview Cheating? covers the spectrum from the employer's perspective. The two posts address opposite ends of the same question: know which scenario you are walking into.

If you want to understand the broader shift in how AI fluency is evaluated across roles, How to Show AI Fluency in an Interview covers sample answers for SWE, PM, and analyst contexts.

PokeBot's technical mock modes run you through the full AI-allowed loop with feedback on where your prompts could be more specific and whether your explanation covers the decisions your code makes.

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