Production AI Engineering
Field notes for the moment when a demo becomes a customer-facing system: the questionnaire arrives, RAG answers drift, evals need to gate releases, tools gain permissions, or a model provider changes underneath you.
Enterprise-Ready AI Security
A practical operating model for answering AI security questionnaires with architecture facts, runtime controls, evidence owners, and refresh triggers.
Technical reference library
Each article is written around a production decision, with answer blocks, operational examples, comparison tables, current source links, and a practical next step.
AI Reliability Engineering
A stage-by-stage debugging method for separating parsing, retrieval, reranking, context assembly, citation, and generation failures in production RAG.
AI Reliability Engineering
A practical pattern for turning production failures into versioned eval cases, fast pull-request checks, calibrated graders, and safe release decisions.
AI Reliability Engineering
A production checklist for remote MCP servers: authenticated principals, tenant-scoped authorization, constrained tools, egress controls, audit trails, and failure containment.
AI Reliability Engineering
A production migration method for provider swaps, model upgrades, and Anthropic-to-open-source moves with parity evidence, staged traffic, and rollback.
Have a production failure now?
30 minutes. No deck. Leave with a clear next step.
