Data Modeling for AI Systems

Data science for AI is mostly data modeling. This guide treats features, documents, chunks, embeddings, and evaluation sets as data products with schemas, owners, and lineage. It shows how to model them so training, retrieval, and the application share the same definitions, and how to catch the silent mismatches that degrade results.

What you will be able to do

• Model entities once and reuse the definition across pipelines.
• Structure documents and chunks with metadata that retrieval can filter on.
• Define features and embeddings with explicit lineage.
• Build evaluation sets that match the production data model.

Inside the guide

1. 01

   The data model behind the model

   Why most AI quality problems are definition problems.

2. 02

   Entities and identity

   Keys, resolution, and the single definition rule.

3. 03

   Documents and chunks

   Structure, boundaries, and metadata for retrieval.

4. 04

   Features and embeddings

   Lineage, versioning, and reproducibility.

5. 05

   Evaluation data

   Golden sets that reflect the real distribution.

From the guide

A chunk is a data product. It has a source, a boundary, a version, and a set of metadata that decides whether it can be retrieved at all. Treat it like a row in a table someone depends on, because that is what it is.

Who it is for

• Data scientists moving from notebooks to production AI systems.
• Engineers who own ingestion and indexing pipelines.
• Analytics teams asked to 'add AI' to existing data platforms.