Yesterday, during my trainee conversion interview, I was asked something that sounded pretty simple at first:

“How does AI help in the SDLC?”

I answered it based on what I usually do—faster coding, easier debugging, learning things quicker.

It was fine.

But after the interview, the question just… stayed in my head.

On the way back, I kept thinking about it. And then it clicked:

I haven’t just been using AI in one phase… I’ve kind of been using it everywhere without even realizing it.

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It started with something small

A few days ago, I was working on a small feature—just integrating an API and handling some edge cases. Nothing fancy.

Normally, I’d:

  • -Go through the docs
  • -Try to figure out what could go wrong
  • -Write some code
  • -Fix things when they break

This time, I did something different.

I dropped the API docs into AI and asked:

> “What edge cases should I think about here?”

And honestly, the response surprised me.

It pointed out things like:

  • -Timeout scenarios
  • -Partial failures
  • -Weird/unexpected responses

Stuff I might have thought of… but probably not all of it.

That’s when I realized—this was basically my requirement + design thinking, just way faster.

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Development felt… smoother

While coding, I ran into a bug (as usual).

Earlier, I would’ve spent a good 30–40 minutes trying random fixes.

This time, I shared the error and some context.

The interesting part?

AI didn’t just throw a solution—it explained why it was happening.

And that made a difference.

It didn’t feel like copying code. It felt like I was still learning, just not getting stuck.

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Testing didn’t feel like a chore for once

I’ve never really enjoyed writing test cases.

But this time, I tried:

> “Generate unit tests for this function, including edge cases.”

And it gave me:

  • -Basic scenarios
  • -Boundary conditions
  • -Even some odd edge cases I hadn’t thought about

It actually made my code feel more solid, not just “done”.

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Then came the real pain point: logs

During deployment, something broke.

And like always, the logs were just… chaos.

Instead of going line by line, I pasted them into AI and asked:

> “What’s going wrong here?”

Within seconds, it:

  • -Highlighted the likely issue
  • -Pointed to a misconfiguration
  • -Suggested what to fix

That’s when it really hit me—AI is insanely useful for log analysis.

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Maintenance felt less painful too

A few days later, there was an issue in the same project.

Usually, this means digging through logs, chats, and trying to piece things together.

This time, I used AI to:

  • -Summarize what happened
  • -Figure out the root cause
  • -Think about what could be improved

It made the whole process feel… lighter.

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Looking back

When I think about it now, AI was there in every step:

  • -Understanding the problem
  • -Thinking through the solution
  • -Writing code
  • -Testing it
  • -Debugging issues
  • -Even after deployment

Not in a loud way. Just quietly helping.

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So how does AI help in SDLC?

Not by replacing anything.

But by making every step a little less painful.

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My takeaway

At this point, AI doesn’t feel like just a tool.

It feels more like:

> That one teammate who helps you think clearly, move faster, and not get stuck for too long.

And yeah… maybe this is what I should’ve said in the interview 🙂