AI Interview Questions for Software Engineers

I use AI-generated code as a fast draft, not as trusted output. I look at the problem type first. Boilerplate, tests, migrations, small utilities, and refactors are usually good candidates. Security-sensitive logic, billing flows, auth, concurrency, and performance-critical paths need much tighter review. Then I validate three things: correctness, maintainability, and fit with our codebase conventions. If the code solves the problem but introduces a new pattern nobody else uses, I rewrite it. If it saves time and still reads like something a teammate would maintain six months from now, I keep it. My rule is simple: AI can accelerate implementation, but I still own the architecture, edge cases, and production quality bar.

I review AI-generated code the same way I review a human PR, but with extra skepticism around hidden assumptions. First I restate what the code is supposed to do. Then I test whether it actually does that across normal cases, edge cases, and failure cases. I scan for security issues, unnecessary abstraction, missing validation, weak error handling, and invented dependencies. After that I compare it against surrounding code to make sure the style, naming, and architecture are consistent. Finally I run or write tests that prove the behavior. The important mindset is that AI often produces plausible code faster than it produces correct code. So my process is not about admiring fluency; it is about systematically finding where the code could fail in production.

I would describe RAG as a way to give a language model fresh, relevant context at request time instead of hoping the answer is already baked into the model. The system usually works in four steps: index documents, retrieve the most relevant chunks for a user query, attach those chunks to the prompt, and ask the model to answer from that context. The advantage is that you can ground responses in your own docs, policies, or product data without retraining a model. It is often a better first step than fine-tuning because it is faster to update, easier to debug, and safer for factual tasks. The engineering work is mostly in chunking, retrieval quality, ranking, prompt design, and evaluation.

I use AI as a second brain, not as a replacement for debugging discipline. I still start by reproducing the issue, narrowing the failing path, reading logs, and understanding the system boundaries. Then I use AI for targeted tasks: explaining an unfamiliar stack trace, suggesting likely root causes, summarizing a large file, or generating hypotheses to test. The important part is that I do not accept the first explanation just because it sounds intelligent. I verify every suggestion against the code and the runtime behavior. If AI helps me think faster, great. If it starts steering me toward random speculation, I reset and go back to first principles. Good engineers use AI to speed up diagnosis, but they still anchor on observable facts, not fluent guesses.

I would start with a narrow, low-risk use case where AI is assistive rather than fully autonomous. Then I would put it behind clear boundaries: separate service layer, explicit input and output contracts, logging, retries, rate limits, and kill switches. I would design graceful fallback behavior so the product still works if the model times out, returns low confidence, or produces unusable output. I would also add instrumentation before launch: latency, acceptance rate, user overrides, error categories, and cost per request. Finally, I would launch it gradually, ideally as an optional beta, and review real usage before expanding scope. The mistake teams make is wiring AI deeply into a critical path before they understand its failure modes. I prefer an architecture that lets us learn safely, then increase automation when trust is earned.

I separate the problem into data exposure, code execution risk, and supply-chain risk. For data exposure, I avoid pasting secrets, production incidents with customer data, or sensitive contracts into tools unless the policy explicitly allows it. For code execution risk, I do not let generated commands or migrations run unchecked; they get reviewed like any other change. For supply-chain risk, I verify every dependency AI suggests because models often recommend outdated or unnecessary packages. I also want clear organizational policy: approved tools, data handling rules, retention settings, and which repositories are allowed. On top of that, I keep auditability in mind. If AI helped produce a change, the final code still needs a human owner who understands it well enough to maintain it and defend it in review.

I start with the simplest system that can reliably solve the problem. If the task is deterministic, like validation, routing, or calculations, I use traditional software first. If the task is language-heavy but can be solved with a strong prompt and no private knowledge, prompt engineering is enough. If the model needs access to changing internal knowledge, I reach for RAG before fine-tuning because it is easier to update, inspect, and test. Fine-tuning only enters the picture when we need consistent behavior, domain-specific style, or structured performance that prompting and retrieval cannot deliver. The key is not choosing the most sophisticated AI technique. It is choosing the lowest-complexity architecture that meets the accuracy, latency, and maintenance requirements.

A strong evaluation strategy combines offline tests, live product metrics, and human review. Offline, I want a curated eval set that reflects real developer tasks: bug fixing, code transformation, test generation, and explanation quality. I score correctness, completeness, formatting, and harmful failure modes. In production, I measure acceptance rate, edit distance from generated output to final output, latency, abandonment, and negative feedback. I also segment by task type because a feature can look good in aggregate while failing badly on one workflow. Finally, I keep a human review loop for surprising failures and new patterns. AI features drift over time as prompts, models, and usage change. So evaluation is not a one-time benchmark. It is an ongoing engineering system for noticing when quality is improving, flat, or quietly breaking.

I would treat that as a process and quality problem, not just a tooling problem. First I would confirm where the quality drop is happening: weaker tests, more architectural drift, more review churn, or more regressions after merge. Then I would tighten the workflow around the tool. That could mean better prompts, stronger PR templates, mandatory tests, clearer coding standards, or limiting AI assistance for certain classes of work. I would also coach engineers on when not to use the tool. Faster output is only valuable if review and maintenance costs do not erase the gain. If the assistant increases churn more than throughput, I would narrow its allowed use cases until we find a better operating model. The goal is leverage, not just speed theater.

I would design layered guardrails. At the input layer, restrict which repos, branches, and file types the agent can touch. At the action layer, require explicit approval for destructive operations, dependency changes, credential-related files, and infra code. At the output layer, run validation checks such as tests, linting, schema checks, and policy scans before changes can proceed. I would also keep strong observability: prompts, diffs, commands run, files touched, and rollback paths. The most important architectural decision is limiting blast radius. The system should fail safely, produce explainable diffs, and be easy to interrupt. Autonomy is useful, but only if humans can understand what happened and stop it before a confident mistake becomes an expensive incident.

I assume this will happen regularly, so I build habits and systems around verification. Personally, I cross-check generated claims against source code, official docs, and runtime behavior. At the product level, I reduce the chance of confident nonsense by grounding the model with relevant context, asking for structured output, and constraining the answer to what the system can actually know. If the feature is user-facing, I want fallback behavior for uncertainty and a way for users to inspect or correct outputs. The mistake is treating fluency as evidence. Good engineering judgment means recognizing that a beautiful explanation can still be wrong. I would rather ship a narrower system that is reliably useful than a broad one that sounds smart while quietly misleading people.

Clear interfaces, strong tests, readable code, and good documentation become more important because AI amplifies both good and bad environments. If the codebase has consistent patterns, AI can help faster. If the system is chaotic, AI produces more chaos at higher speed. I also think design reviews and postmortems become more valuable, because teams need shared judgment about where automation helps and where it creates risk. Engineers sometimes assume AI lowers the need for rigor because it can generate so much so quickly. In practice, the opposite is true. When output becomes cheaper, discernment becomes more expensive and more important. The teams that win are not the ones that generate the most code. They are the ones that preserve standards while moving faster.