Data ModelingComing soon
Data Modeling for AI Systems
Bringing data modeling discipline to features, documents, chunks, and embeddings so every part of the system agrees on what the data means.
Data Modeling
Most AI failures trace back to data that was never modeled: inconsistent entities, undefined fields, and features nobody can reproduce. This topic brings data modeling discipline to AI work, from source schemas through feature definitions to the documents and chunks a retrieval system sees.
Questions this topic answers
- 01
How do you model entities so that training data, retrieval data, and application data agree?
- 02
What is a feature, a chunk, and an embedding, in data-modeling terms?
- 03
How should documents be structured before they are indexed?
- 04
Which metadata makes retrieved content trustworthy and filterable?