Why this format changes the interview
In a conventional coding round, planning and typing limit how much implementation can be discussed. With an agent producing code quickly, the interviewer can spend more time examining the decisions behind the implementation. The task can progress through additional requirements, stronger tests, hardening work, and more advanced extensions.
Some requirements may be deliberately open to interpretation. That is an opportunity to show that you noticed a decision had to be made. A strong candidate calls out the assumption, considers other reasonable options, and chooses a direction instead of inheriting the model's default without discussion.
These questions do not depend on a reference implementation. Coding agents can produce strong solutions in different forms, so the interviewer focuses on how you inspect the result, explain its behavior, verify it, and identify what still needs attention.
Expect the question to require you to:
- Work through more than the initial functional requirement.
- Recognize where the prompt leaves an engineering choice open.
- Check the generated implementation and its test results.
- Explain selected code in detail without relying on the agent.
- Discuss cases and production concerns the first draft overlooked.
The three areas that matter most
A strong result starts with working code. Interviewers then look at how well you review and defend it: whether you understand the implementation, can evaluate its decisions, and can demonstrate that it behaves correctly. There is no required sequence of prompts; what matters is that you genuinely own the completed work.
1. Judgement
Judgement is your ability to form an independent view of the generated solution and redirect the work when needed.
Positive signals
- You read the output before accepting it and revise errors or weak decisions.
- You surface choices the model made on its own and compare them with other viable approaches.
- When the result is wrong, you develop a diagnosis and use it to guide the next change.
- You remove unsuccessful fixes instead of leaving abandoned or conflicting code behind.
- You pause and change direction when the implementation is moving down the wrong path.
Negative signals
- Generated changes are accepted without a meaningful review.
- Model-selected defaults are treated as inevitable rather than as decisions that could be challenged.
- Failures lead to repeated prompts or retries without an explanation of the likely cause.
- The repository accumulates unused or contradictory fixes.
- An unproductive direction continues without reassessment.
2. Understanding
Understanding means being able to reason about the generated code as if you had written it yourself.
Positive signals
- You can trace the implementation and explain why it was structured that way when the interviewer selects a specific area.
- Your explanation includes your own technical reasoning, not a paraphrase of the agent’s response.
- You can describe the benefits and costs of the approach that was chosen.
- You find missing cases or defects during your review.
- You connect the implementation to concerns beyond the narrow example in the prompt.
Negative signals
- You cannot explain selected code without asking the agent to do it for you.
- Your explanation goes no further than what the model already said.
- The reasons behind important implementation choices are unclear.
- The code is assumed to be correct because it looks plausible.
- Discussion stops at the straightforward success case.
3. Production Reasoning
Production reasoning is the ability to examine how the solution behaves under failure, load, and day-to-day operation—not only when the main example succeeds.
Positive signals
- You identify failures the generated version did not cover and explain how much of the system or user experience each one could affect.
- You inspect buffers, counters, and collections for missing limits.
- You ask what logs, metrics, or alerts would make a live problem diagnosable.
- You describe how the feature should respond when its primary route is unavailable.
- You recognize that a green test run does not answer every question about behavior in a real environment.
Negative signals
- Only failures already represented in the code are discussed.
- Memory use and other resource limits are not considered.
- There is no discussion of how a live issue would be detected.
- No alternative behavior is considered when the main route fails.
- Passing tests are presented as the full correctness argument.
How you work is supporting evidence
The technical areas above are easier to recognize when your execution is deliberate and your reasoning is clear. Interviewers also observe whether you can move the task forward independently and communicate a credible rationale for your decisions.
Executing the work
- Before implementation, identify any ambiguity that would change the design, state your assumption, and confirm that you are solving the intended problem.
- Leave enough time to read, test, and explain the generated code; producing more code is not useful if you cannot defend it.
- Test often and investigate failures in an orderly, technically sound way.
- Work through most obstacles without depending on interviewer help.
Explaining your approach
- Give an answer that covers the implementation and the reasoning behind it without requiring a long series of follow-up prompts.
- State assumptions and organize the explanation so the main points are easy to follow.
- Continue working constructively after mistakes or failed attempts.
- When the problem is unclear, turn that uncertainty into explicit questions and a workable plan.
- Listen to feedback and reflect it in the next version.
You are not being graded on one prompting style
Engineers use coding agents differently. You might discuss the design before any files change, or you might generate a small first attempt and then inspect it. You might begin with a compact instruction and refine it over several turns, or provide more constraints at the beginning.
None of these choices is automatically stronger. The relevant outcome is a sound implementation that you have examined and can explain. Do not spend your preparation trying to memorize a perfect prompt. Spend it learning how to take responsibility for whatever the agent produces.
What the interview may look like
In the format described here, the interview runs for 60 minutes on CodeSignal with Claude Code provided. It begins with a starter task and then moves through follow-ups.
A typical round for each stage is:
- Receive the requirement. The interviewer says it aloud and places the same text in the project README.
- Build with the agent. You plan, generate, inspect, test, and revise the implementation.
- Explain the result. You walk through the code, defend important decisions, and point out remaining weaknesses.
- Demonstrate correctness. You show the evidence that supports your claims about the behavior.
- Save a clean checkpoint. Commit the completed stage before the next requirement so you have a known baseline and can spot regressions introduced by later follow-ups.
Completing every extension is not the goal. Two or three stages can provide enough depth for a strong assessment. The interviewer may change the order or omit an extension when the implementation has already addressed it or another area is producing better discussion.
How to practice before the interview
Rehearse one complete interview stage
Do not practice code generation in isolation. For each exercise, follow the same sequence you will need during the interview:
- State the important assumptions and get the code working.
- Review the generated changes and remove failed or unnecessary attempts.
- Explain one selected path in detail and defend one design choice against a reasonable alternative.
- Run the tests, inspect their output, and use a specific input to demonstrate why the behavior is correct.
- Name something the first draft missed: a failure, an unlimited resource, missing production visibility, or an undefined fallback.
- Commit the stage before starting another follow-up.
Treat the first draft as material to inspect
Read the output before deciding it is finished. Find errors, debatable choices, and decisions that came from the model rather than the prompt. For the important ones, practice stating whether you would keep them, what another option would be, and why.
Explain the failure before requesting a fix
When behavior is wrong or a test fails, pause long enough to form a technical hypothesis. Use that reasoning to direct the next change. After an attempted fix does not work, remove it so the code remains coherent.
Be ready to teach the generated code
Practice explaining whichever function or line another person selects, not only the parts you planned to present. Walk through what it does, why it exists, what assumptions it carries, and where it might fail. Your explanation should stand on its own without consulting the agent.
Support correctness claims with evidence
Generating a test suite is not enough. Run it, inspect the results, and connect those results to the behavior you claim. Be prepared for an interviewer to ask you to justify your confidence or trace a concrete input through the implementation.
Practice giving a critical review
After each exercise, state what is strong about the output, what you would revise, and why. Identify the decisions the model introduced and compare them with plausible alternatives.
Look past the normal success case
Ask what breaks when a dependency or internal step fails. Search for state that can keep growing, consider what information would help debug a live incident, and describe what the system should do if a recomputation never finishes.
A practical checklist for the interview
- State and defend decisions while you plan, build, and revise.
- Turn unclear requirements into explicit assumptions instead of letting the model choose silently.
- Review both the proposed approach and the generated changes at a point that fits your workflow.
- Run the tests and inspect their output yourself.
- Diagnose problems before redirecting the agent, and remove failed experiments from the codebase.
- Prepare to explain any selected code and give your own view of its trade-offs.
- Consider failures, scope of impact, resource limits, production visibility, and fallback behavior.
- Prefer careful review and a defensible result over speed or a large volume of generated work.
- Create a clean commit before beginning the next stage.
The central shift is from producing code to taking responsibility for it. A strong candidate can examine the agent's work independently, explain how it behaves, show why it works, and describe where it still needs protection.