Hello Reader,

“How are you using GenAI in your day-to-day work?” This question shows up in almost every interview now, and almost everyone gives the same answer.

“I use GenAI to write code.” “I use it to generate documents.” “I have a bot that automates part of my job.”

These are bad answers. Not because they are false, but because they tell the interviewer nothing about you specifically.

The prompt is not the differentiator. Anyone can type a prompt into Claude or ChatGPT and get code back. What the interviewer actually wants to know is whether you apply your own judgment on top of the output, because every one of these models is nondeterministic.

The same prompt can generate a different result on two separate runs. A candidate who blindly trusts the output looks identical to someone who has never touched the tool. A candidate who catches what the model got wrong looks like someone who understands the system underneath it.

Framework for this answer

The framework for this answer is name a specific real-world scenario, explain what GenAI got wrong or generated inefficiently, and explain how your own judgment corrected it. That structure is what separates sounding like a user of AI tools from sounding like someone who directs them.

Here are a few real patterns using the framework that you can adapt into your own answer:

GenAI tends to over-engineer

If you accept a field from program A and just need to insert it into a database, the model often adds validation checks that duplicate what the calling program already handles. Left unchecked, this bloats your codebase. Catching that redundant logic, and explaining why you removed it, shows you are thinking about long-term maintainability, not just shipping something that runs.

Inconsistent infrastructure as code generated by GenAI

One run might open a security group to the entire internet. One time it generates YAML, the next run JSON. The fix is not catching this manually every time.

Say that you built a skill, a reusable instruction set, that tells the model to always generate CloudFormation in YAML, never leave a security group open to the internet, and require additional approval for any internet-facing load balancer. Naming “skills” and “nondeterministic” by name signals you are current with the latest patterns, not just the basics.

GenAI misses database indexes it should use

Here is an answer you can adapt directly: “I was coding a serverless microservice using API Gateway, Lambda, and DynamoDB. My table had both a primary index and a global secondary index. When I asked GenAI to add a new feature, it generated a scan instead of a query.

A scan reads the entire table with no index, which is expensive at scale. I caught that it ignored the GSI and told it explicitly which fields to query on instead.” That answer shows you review AI-generated code for performance, not just correctness.

GenAI makes small factual errors in slides and diagrams

A common one: claiming spot instances save up to 75 percent, when that number actually applies to reserved instances. Spot savings typically run over 90 percent. On system diagrams, a frequent error is showing SQS invoking SNS, when the correct direction is SNS publishing to SQS.

Catching these inversions signals you understand the actual services, not just what the tool generated about them.

A more advanced move:

when generating a system design, explicitly ask for official AWS documentation links for every service and step, and instruct the model to skip unofficial sources.

There is enough noise online that models pull from outdated or incorrect blog content. Requiring official docs as a validation step reads as real production discipline.

One habit that consistently improves output

List every requirement up front instead of feeding instructions one at a time. If you need a microservice enhanced with a discount feature, tell the model up front to reuse the existing DynamoDB tables and indexes, query instead of scan, reference the specific CloudFormation file for the GSI list, and check for other APIs the change might touch.

Giving the full picture at once produces dramatically better output than correcting the model step by step, and it reduces token cost since re-explaining context repeatedly burns more tokens each time.

The next time someone asks how you use GenAI, skip “I use Claude to code.” Pick two or three of these patterns, adapt them to your own experience, and give an answer that actually demonstrates judgment. That is the answer that gets remembered after the interview ends.

Keep learning and keep rocking 🚀,

Raj

P.S - If you want to get an AWS Solutions Architect job without coding or learning every AWS service, the 10th cohort for AWS SA Bootcamp is launching on Oct 17th, 12 PM ET (Eastern Time) via live workshop. This program now includes our updated Gen AI - including FDE roles! Please register below:

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