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Redesigning enterprise infrastructure to support real-time LLM integration at scale — routing, safety, and observability patterns we use in production.
Overview
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At a glance
Key takeaways
- Split the stack: treat ingress, policy, and model execution as separate concerns so LLM traffic stays observable and replaceable.
- Route with intent: use health-aware paths and regional pools for latency and residency without one overloaded API.
- Trace what shipped: bind prompts, model revisions, and sessions to trace IDs so incidents map to a specific release.
FAQ
Generative Intelligence Architecture— common questions
Practical answers for teams shipping LLMs—routing, latency, safety, and when to scale out inference.
What is generative AI architecture for enterprise production?
How do you reduce latency in LLM inference pipelines?
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