Before fine-tuning.
Before RAG.
Before prompts.
You need data.
If you want an LLM Twin that writes like you, the system must first collect your digital footprint from everywhere.
Medium, Substack, LinkedIn, GitHub… all of it.
⚙️ Use ETL for data collection
The cleanest design is the classic pipeline:
Extract → Transform → Load
Extract → crawl posts, articles, code
Transform → clean & standardize
Load → store in database
This is your data collection pipeline.
🗄️ Why NoSQL works best
Your data is not structured.
text
code
links
metadata
comments
So a document DB fits better than SQL.
Example:
MongoDB
DynamoDB
Firestore
Even if it's not called a warehouse,
it acts like one for ML.
đź“‚ Group by content type, not platform
Wrong design:
Medium data
LinkedIn data
GitHub data
Better design:
Articles
Posts
Code
Why?
Because processing depends on type, not source.
articles → long chunking
posts → short chunking
code → syntax-aware split
This makes the pipeline modular.
Add X later?
Just plug new ETL.
No rewrite needed.
🧠Why this pipeline matters
Good data pipeline = good LLM Twin
You get:
cleaner training
better RAG
easier fine-tuning
modular architecture
scalable system
Most people start from the model.
đź’–Real systems start from the data.đź’–
Top comments (4)
This is a brilliant approach to building an LLM Twin. The focus on clean, modular data pipelines is spot on. Prioritizing data collection, organization, and storage before diving into fine-tuning or RAG ensures that the foundation is solid. Grouping by content type rather than platform is a game-changer—this modular approach makes scaling and extending the system a breeze. In production ML, data quality and structure are everything, and this approach nails it. Truly fantastic insight into how real systems should be designed.
Excellent.
Excellent!
Good!