#system-design

From a system perspective, we summarize that the essence of the LLM function is not the prompt statement itself but the boundary, contract, state, and failure handling.

LLM quality is stabilized when managed through datasets, evaluation criteria, online feedback, and regression detection loops, not sentence tuning.

We summarize the reasons and operating patterns for retries, timeouts, fallbacks, and circuit breakers in LLM systems that should be designed differently from regular APIs.

LLM costs are determined by the system control method, not the model unit price. Organize cache, batching, routing, and token budget from an operational perspective.

LLM security is not solved by prompt defense alone. Covers system design that combines permission policies, data boundaries, and tool sandboxing.

To catch quality degradation without failure in LLM operation, trace, log, and quality indicators must be designed as a single observation system.

LLM quality is more sensitive to the context path than the model. We summarize how to design RAG, memory, freshness, and tenant boundaries from a system perspective.

Operable agents are state-based systems, not chains. Planner/Executor separation, queues, guardrails, and recovery strategies are organized from a practical perspective.

It covers failure UX, human intervention, and operational governance design to make a technically functional LLM function into a trustworthy product for users.

We summarize why different deployment gates are needed for each type of change in LLM operations, as well as experiment, canary, and rollback strategies.

We present an LLM/Agent reference architecture that combines prompting, evaluation, reliability, cost, security, and observability into one operating system.

We present an organizational structure, role separation, decision-making system, and maturity roadmap to operate the LLM/Agent system sustainably.