Notes from production AI work.
AI Engineering Field Notes
Practical notes on building production-grade AI systems: RAG, LLM evaluation, agentic workflows, platform architecture, governance, developer experience, and engineering leadership in enterprise environments.
The demo is not the system.
These notes focus on what changes when AI has to be secured, governed, evaluated, operated, and owned by real teams.
Production RAG: what matters after the demo
A practical checklist for turning retrieval-augmented generation from a convincing prototype into a governed, observable, useful product.
Agentic AI in regulated enterprises
Where agentic workflows help, where they create risk, and how to design approval, auditability, and failure boundaries.
Evaluating LLM applications in production
How to evaluate quality, risk, regressions, and usefulness when LLM outputs are probabilistic and context-dependent.
AI platform architecture for enterprise teams
Reference patterns for moving from notebooks and isolated tools to reusable platforms that help many teams ship safely.
Need AI systems that survive real users, real data, and real ownership?
These notes are for teams moving from impressive demos to software they can operate, audit, and improve.