Designing High-Precision LLM RAG Systems: An Enterprise-Grade Architecture Blueprint

A contract-first, intent-aware, evidence-driven framework for building production-grade retrieval-augmented generation systems with measurable reliability and bounded partial reasoning. Executive Overview Most RAG (Retrieval-Augmented Generation) systems fail not because models are weak — but because architecture is naive . The typical pipeline: User Query → Retrieve Top-K → Generate Answer works for demos. It collapses in production. Enterprise environments require: High answer usefulness under imperfect evidence Strict hallucination control Observable and explainable decisions Stable iteration without regressions Measurable quality improvement over time A high-precision RAG system is not a prompt pattern. It is a layered, contract-governed, decision-aware platform . This blueprint defines how to build such a system. 1. From Chatbot to Answer Platform A production RAG system must operate across three realistic states: State Description Fully answerable Sufficient evidence exists. Part