Build the foundation that gives AI systems reliable inputs: data pipelines, documentation, business rules, permissions, memory, retrieval, and source-of-truth handling.
5 articlesA practical playbook for building reliable data foundations that feed AI systems. Covers data pipeline architecture, documentation quality standards, business rules encoding, and the source-of-truth patterns that separate working RAG from hallucinating RAG.
Build RBAC and row-level security into RAG pipelines without crippling your agents. Covers pre-retrieval filtering, permission propagation, delegated identity, and the hard tradeoffs between access breadth and data boundaries.
Documentation quality directly determines AI output quality. Teams that treat docs as infrastructure — structured for machines, versioned like code, tested for freshness — gain a compounding advantage every time an AI agent touches their codebase.
