I built a RAG feature pipeline thinking it would be clean:
“Just take raw data, process it, generate embeddings, store in vector DB… done.”
Yes.
“Done.”
Step 1: Clean the Data (aka emotional damage)
I opened my dataset.
It had:
broken text
random HTML
sentences that started in 2012 and ended in 2026
So I cleaned it.
Then cleaned it again.
Then realized:
“Cleaning data is just debugging… but slower.”
Step 2: Chunking (aka cutting things you don’t understand)
Now I had to split text into chunks.
Too big → model confused
Too small → model useless
So I picked a size and said:
“Looks reasonable.”
(It wasn’t.)
Step 3: Embeddings (aka turning words into math magic)
I converted text into vectors.
Thousands of them.
They looked like:
[0.123, -0.928, 0.44, …]
I nodded like I understood.
I did not.
Step 4: Store in Vector DB
Everything went into the database.
Fast. Scalable. Beautiful.
Until I queried it.
I asked:
“Find relevant context.”
It returned:
Something… technically related.
Emotionally unrelated.
Final Lesson
A RAG pipeline is not:
just cleaning
just chunking
just embedding
It’s:
making sure your future self doesn’t question your life choices.
Truth
If your RAG output is bad…
It’s not the model.
It’s your pipeline.
And that’s when I realized:
I didn’t build a feature pipeline.
I built a system that politely reflects my bad decisions… at scale.
Top comments (2)
Wow! Your writing always captivates me! It's truly wonderful. Thank you once again. Please continue to post many more great pieces!!! ✨✨✨
Good.