Think of it as Yoga for the brain, we are going to stretch a bit.
🧠How to Design a
Software System
using AI (The Right Way)
Why do you ask AI to build the house before you drawn the floor plan? --YNOT!
Most people treat AI like a magic vending machine: shove in a vague idea, press the button marked code, and hope a working business falls out. Then they act surprised when the machine hands them a shiny little disaster wired together with guesswork and optimism. The smarter way is slower for a minute and faster for a month: think first, design the workflow, name the parts, define the states, and only then bring AI in like a very fast carpenter who finally has a blueprint worth following. AI is powerful, but it has the same weakness as every eager assistant in history—it will happily build exactly what you asked for, even when what you asked for was nonsense.
That is the real lesson here: software is not born from code any more than a building is born from hammer swings. It starts with thought, structure, sequence, and clear intention. Once you know what the system must do, how the information moves, and where each piece belongs, AI stops being a novelty and starts becoming leverage. Then you are not begging a machine to “code, code, code” like a man yelling at a piano and expecting a symphony. You are guiding it step by step—from idea, to architecture, to specification, to templates, to implementation. And that little change makes all the difference, because when you design first, you do not just get more code. You get a system that has a fighting chance of making sense tomorrow.
From Idea → Architecture → Specification (Without Writing a Single Line of Code)
Most people do this backwards.
They get an idea… They open their editor… They start coding… …and three weeks later they’ve built a mess they can’t scale, can’t maintain, and don’t fully understand.
What we just did was the opposite. We didn’t write code. We designed the system first.
🔥 Step 1: Start With the Outcome, Not the Code
We didn’t begin with:
Python
Flask
SQL
APIs
We started with one simple question:
👉 What do we actually want this system to do?
The answer:
Scrape news
Review it
Rewrite it
Organize it
Publish it into WordPress
That’s it. No tech yet. Just behavior.
🧱 Step 2: Define the Real-World Workflow
Before databases… before tables…
We mapped the human process:
Find stories
Store them
Review them
Select the best ones
Rewrite headlines
Arrange layout
Publish
That’s critical. Because software is nothing more than a structured version of a real-world workflow. If you don’t define the workflow first, your code will fight you later.
🧠 Step 3: Identify the Core Objects (This Is Where Most People Fail)
We asked: 👉 What are the fundamental things in this system?
We didn’t guess—we extracted them from the workflow.
We got exactly three:
Sources → where data comes from
Stories → the raw + edited content
Summaries → the final output
That’s your data model.
Not 20 tables. Not over-engineered nonsense.
Just the minimum set of real-world objects.
🧩 Step 4: Define Responsibilities (Separation of Concerns)
Each object has a job:
Sources → define how to scrape
Stories → hold all content + editorial state
Summaries → represent final output
This is where structure emerges. You’re not just storing data—you’re defining behavior boundaries.
⚙️ Step 5: Design the Workflow States (Instead of Chaos Flags)
Most systems break here.
People add fields like:
is_selected
is_rejected
is_published
is_featured
is_main_story
That becomes a nightmare. Instead, we defined a single state machine:
new → reviewing → selected → packaged → published
Now:
no confusion
no conflicting flags
clean logic
🧱 Step 6: Design for Scale Before Writing Code
We made one critical decision early:
👉 Each WordPress site is independent
That led to:
prefix-based table design
no shared datasets
per-site configuration
Example:
mm_wp_news_stories
alien_wp_news_stories
This one decision prevents:
data collisions
cross-site bugs
future rewrites
🗄 Step 7: Design the Database Last (Not First)
Only after all that… We built the schema.
Because now we knew:
what data exists
how it flows
how it changes
who interacts with it
So the database became obvious. Not guessed. Not forced.
🧠 Step 8: Keep It Simple Where It Matters
Example:
Instead of building a complex join table for summaries, we used:
[123, 456, 789]
Why?
Because:
faster to build
easier to debug
good enough for V1
You don’t over-engineer early. You build for clarity and momentum.
🔗 Step 9: Define Integration Points Early
We knew this system must connect to WordPress.
So we planned:
wp_posts linkage
draft vs publish
media handling
Before writing code. That prevents rework later.
🚀 Step 10: Only Now Do You Write Code
At this point:
data model is clear
workflow is clear
architecture is clear
edge cases are mostly known
Now coding becomes: 👉 implementation, not exploration
That’s the difference between:
hacking something together
building a system
💡 The Big Insight
Most people think software development is:
👉 Writing code It’s not.
👉 It’s: Designing systems
Code is just the final translation.
⚡ Why This Method Works
Because it forces you to:
think in workflows
think in data
think in systems
eliminate ambiguity
Before complexity is introduced.
🧠 Final Thought
If you ever feel like your codebase is getting messy…
It’s not a coding problem. It’s a design problem.
And the fix is simple:
👉 Stop coding.
👉 Go back to the system.
👉 Redesign it properly.
