AI-Ready Data
Published by Self-published · Tech
Designing data platforms optimized for LLMs, AI agents, and retrieval-augmented generation (RAG) workflows.
Who this book is for
Data engineers, AI and ML engineers, platform architects, and technical leaders building the data infrastructure AI systems depend on. It is about the data layer, not models, prompts, or fine-tuning.
Table of contents
- What "AI-Ready Data" Means
- Data Quality for AI
- Structuring Data for RAG
- Embeddings and Vector Infrastructure
- Semantic Layers for AI
- Knowledge Graphs as Data Infrastructure
- Feature Stores and Embedding Registries
- Open Lakehouse Architecture for AI
- AI Data Pipelines
- Data Governance for AI
- Observability for AI Data Pipelines
- Putting It All Together
Excerpt
This book was written for data engineers, AI engineers, platform architects, and technical leaders who are building the data infrastructure that AI systems need to succeed. It is not a book about models, prompts, or fine-tuning. It is a book about the data layer that determines whether those models, prompts, and fine-tuning efforts produce reliable results. The premise is simple: most AI failures are data failures. A Retrieval-Augmented Generation system that retrieves stale documents produces stale answers. An AI agent that cannot discover or understand your data returns generic responses. A classification model trained on biased data produces biased predictions. Fixing any of these problems requires fixing the data, not the model.
