AI & LLM Systems — production notes.
Depth-first writing on RAG pipeline architecture, LLM evaluation, production drift, governance, and the engineering decisions that separate demo systems from production ones.
What you need to know about ai & llm systems
Retrieval-Augmented Generation (RAG) is now a default pattern for AI deployment in regulated environments — but most teams discover its failure modes only in production. Embedding drift, retrieval quality degradation, and the absence of evaluation harnesses mean that RAG systems that pass offline tests routinely fail live users within weeks of launch. Building AI systems for financial services, healthcare, or enterprise compliance requires governance to be part of the architecture from day one: PII boundaries in retrieval pipelines, audit trails for every model call, and offline evaluation datasets that reflect real production distributions. AiRAT's engineering notes in this cluster cover the engineering discipline behind production-grade AI — from evaluation harness design to governed LLM deployment under UAE Central Bank, RBI, and EU regulatory constraints.