I decided to build an LLM Twin using a clean ETL + FTI architecture, thinking it would be structured, scalable, and elegant.
It started well.
I designed a proper ETL pipeline:
extract data from blogs, GitHub, and posts
clean and normalize everything
store it nicely in a database
Simple, right?
Then reality happened.
My “clean data pipeline” slowly became:
random HTML scraping
inconsistent formats
mysterious edge cases
But technically…
it was still an ETL pipeline 😅
The idea was smart though:
Instead of overcomplicating things, I reduced everything into just three types:
articles
repositories
posts
Which meant I could scale easily later without rewriting everything.
That part actually worked.
But here’s the funny part.
I thought I was building a system that understands data.
What I really built was a system that shows me:
how messy real-world data is
how optimistic my assumptions were
and how “simple architecture” becomes complex in 2 days
Final Thought
You don’t build an LLM system in one go.
You:
build something messy
make it work
then slowly make it make sense
And somewhere along the way…
your “LLM Twin” starts looking less like a tool,
and more like a mirror of your own engineering decisions.
Top comments (5)
The final thought is the best part — 'a mirror of your own engineering decisions.' That's what makes building with AI so revealing. The chaos isn't in the data. It's in the assumptions you didn't know you were making. Did the messy reality end up changing your original architecture, or did you mostly patch around it?
Good
Good
Great.
good!