RAG Data Foundations

Course overview

Foundational RAG concepts for AI data engineers: architecture, limits and source governance. Covers source inventory, data ownership, freshness, ACLs, parsing common formats (PDF, HTML, Markdown, CSV) and metadata design for citations and filtering.

Describes RAG architecture, when RAG is inappropriate, and core considerations (key concepts and takeaways).

Details source inventory, data ownership, freshness controls and ACLs to support reliable RAG pipelines.

Covers practical parsing approaches for PDFs, HTML, Markdown, CSV and structured records and their impact on indexing.

Outlines metadata design principles to enable accurate citations and effective filtering in RAG retrievals.