How to Hit 200ms on RAG-Grounded Responses
Why You Should Not Pick One Foundation Model
Production AI without evaluation is production AI you hope is working. The 3 evaluation dimensions every system needs (retrieval, faithfulness, end-task accuracy), the 5 patterns that keep evaluation continuous, and the 4-layer architecture.
Your CFO will ask 5 specific questions about AI in 2027. The 4 audit artifacts that answer them and the artifact chain pipeline that turns the audit into a database query.
Most mid-market AI use cases under 100k chunks do not need a vector database. The 3 architecture patterns (in-memory, embedded, dedicated), the 5 selection criteria when you do need one, and the architecture that makes vector storage decisions reversible.
RAG and fine-tuning solve different problems but most teams treat them as interchangeable. RAG wins 85-90% of mid-market use cases; fine-tuning wins for domain language, format conformance, latency bounds. The 3 cases fine-tuning wins, 5 RAG patterns, 4-layer architecture.
Hotels are using AI to replace 40% of operational headcount in specific categories while keeping guest satisfaction stable. The 5 workflows, integration patterns, and workforce transition discipline.
Production AI is not a hardened prototype; it is a different architecture with 7 explicit layers. The data, model abstraction, retrieval, prompts, evaluation, cost, and observability layers prevent specific failure modes. Build them from day 1 or rewrite within 12 months.
Most legacy modernization plans are scoped as code rewrites. The 2026 reality: 60 to 80% of legacy workflows should be replaced or augmented with AI, not rewritten.
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