AI-assisted planning, architecture exploration, implementation option exploration, design docs, technology evaluation, and task breakdown before coding

AI didn’t replace my development workflow. It changed the way I approach it.

AI coding tools have changed dramatically in the last few years.

Today, developers can generate components, debug errors, explore UI ideas, write documentation, and even make changes directly in a Git repository with the help of AI.

But there is one question I kept asking myself:

How can I actually use AI in my daily projects without becoming dependent on it?

After experimenting with different tools, I eventually developed a workflow that works well for me.

Instead of asking one AI tool to do everything, I use different tools for different stages of the project.

My current workflow looks like this:

Idea → ChatGPT → Stitch → MCP → Antigravity → Jules → Human Review

This article explains how I use each part of that workflow and what I have learned from it.

The way we use AI while building a feature has changed quite a bit.

Earlier, we mostly thought of AI as something that helps us write code faster.

Now, we try to bring it into the process much earlier.

For a new feature, we first give AI the context it needs — relevant code, documentation, architecture, and our project-specific skills/guidelines.

Then we use Plan Mode and ask:

“Understand the existing implementation and documentation. Identify dependencies, edge cases, assumptions, and missing information. Ask all necessary questions and create an implementation plan. Don’t change the code yet.”

We review the plan, make adjustments, and only then ask it to implement.

After implementation, we use AI again for a different job:

“Review this like a senior engineer. Look for incorrect assumptions, race conditions, failure scenarios, performance issues, and missing tests.”

The interesting part for me is that AI becomes much more useful when we give it a way of working, not just a task.

Skills tell it how we build things.

Documentation tells it how our system works.

Plan Mode helps it reason before changing things.

So instead of:

“Write this feature.”

It becomes:

Understand → Question → Plan → Build → Review

That shift has made AI feel much more like an engineering partner than just a coding assistant.

What has worked best for you when using AI for development?

#AI #SoftwareEngineering #DeveloperProductivity #ArtificialIntelligence #Coding #SystemDesign #Engineering #OneThingToKnow

AI can write code faster than ever.
But speed is not the same as good engineering.
AI can generate an API, build a component, or suggest a database schema in seconds. What it cannot fully understand is the responsibility behind those decisions.
Will the system still perform under heavy traffic?
Is the code secure?
What happens when a dependency fails?
Can another engineer maintain it six months from now?
Does the solution actually solve the customer’s problem?
These are not only coding questions. They require experience, context, and engineering judgment.
I use AI to explore ideas, remove repetitive work, and deliver faster. But I still review the architecture, challenge assumptions, test edge cases, and take ownership of what reaches production.
The role of a software engineer is not disappearing. It is evolving.
Our value is shifting from how quickly we can write code to how well we can make decisions, manage risks, and build systems people can trust.
AI writes code.
Engineers own the outcome.
How has AI changed the way you approach software development?

Go beyond chat in the GitHub Copilot app with these slash commands. They’ll help you plan, collaborate, automate, and customize your dev workflow.

✅3 simple ways on how I use AI as a software engineer at Google in my day to day life.

1️⃣. Understand Faster

Whenever I pick up a new feature or work area, I ask AI to explain the code flow, tell me the services involved and generate sequence diagram.

The main thing I ask for is the exact files and code links of the flow. Then I verify every step, it’s still some manual effort but I am sure of my understanding, I gain the knowledge for future and am 100% confident to make any design choices.

2️⃣. Code Better & Faster

AI is great at writing code. But it might skip some details, like sometimes a function may already exist in the hierarchy but it defines it again.

I usually avoid it with the first step- understanding things in and out. I also prompt AI to review the code as a lead engineer.

The same goes for tests, there can be issues. I always make sure to list down all the edge cases I can think of. Then I compare that checklist against the generated tests to make sure nothing is missing. I once wrote 45 test cases for a 8 line change.

3️⃣. Write Docs Faster

Take design doc for example: I attach all reference docs where I got all the info from & a design template & then I turn on voice mode and just talk and explain my thought process.

I get a really good draft and starting point. From there, I iterate and if it’s not working out in 5 prompts I write some sections manually(great for long term memory).

For me, AI really helps me with everything, has made me so much productive and saves me a lot of time.

There are many other ways I use AI, for example custom agents etc. Let me know in the comments if I should share that next.

#AI #SimpleUseCases #Productivity

Today I've been putting Opus 5 head to head with Sol and honestly the difference is stark. So I started out on just the $20 plan and the first thing I noticed was that running Opus 5 on Extra reasoning was burning through my weekly limit about as fast as Sol on High did on my 5X Codex Pro plan (20$ vs 100$, lol). As for the actual comparison: For planning Opus 5 is way more goal-oriented. No fluff, just a straight coherent plan and it actually keeps you involved the whole time you're designing it, which for me is a good thing. After using Sol since release my AI workflow and project rules had gotten so complicated I'd basically lost track, I didn't really know where things stood anymore. Opus brought me back into the project, now I actually know what's going on. Sol on the other hand just overengineers everything, runs in circles, and loses me completely along the way and unfortunately it produces code I can barely look at without needing to go breathe some fresh air.

To be fair Sol is still a genuinely strong model, it's just better as a reviewer than a planner. There it's excellent.

Opus 5 walks you through its reasoning and stays concrete instead of drifting into pseudo language. Like, I kept catching myself typing "ELI5" at Sol after a long answer, and I'd like to think I'm not that dumb. With Opus I actually read the whole answer and understood it.

So yeah, Sol produces these plans that sound impressively sophisticated, good enough to break into the Pentagon, but by the time it gets there you've blown through your weekly limit.

I have upgraded to 5X CC and downgraded to 20$ Codex so I can still use Luna / Terra for small, well-defined implementation packets.

AI didn’t replace me — it changed the way I think, build, and solve problems.

A few years ago, my day as a developer looked very different.

I would spend hours searching Google for solutions, reading Stack Overflow threads, watching YouTube tutorials, and trying different approaches until something finally worked.

New Jira and Teamwork Graph capabilities help engineering teams plan, assign, govern, and measure work across humans and AI agents.

Everyone is afraid AI will replace developers.

They're asking the wrong question.

The right question is: which parts of "developer" get replaced, and which parts become more valuable?

AI is very good at writing code when the problem is well-defined.

It is not good at deciding what the problem actually is.

It can give you ten ways to build a feature.

It cannot tell you whether that feature should exist.

What's getting replaced is the part of the job that was always the least interesting anyway: writing boilerplate, looking up syntax, building the same CRUD screen for the hundredth time.

What's becoming more valuable is the part that was always hardest to teach: understanding the actual problem, talking to the people who have it, and making judgment calls with incomplete information.

I use AI every day now. It writes a meaningful share of our code.

But the decisions about what to build, why, and for whom.
That's still entirely human, and I don't see that changing.

The developers who'll struggle aren't the ones who use AI.

They're the ones whose entire value was "I can write this code" and nothing else.

AI didn't make me a 10x developer

It removed hours of boring work.

Now I use it to:
• Design APIs
• Generate boilerplate
• Review code
• Find edge cases
• Write tests
• Create documentation

The biggest productivity gain isn't coding.

It's reducing context switching.

How are you using AI in development?