Production LLM systems accumulate reusable operational experience, but the practical deployment issue is not merely whether such experience can help. It is how different serving strategies trade off quality against online cost under realistic constraints. Injecting external experience can improve task quality, yet it also increases prompt burden, latency, and serving pressure. We study \textit{external experience serving} as a deployment-oriented quality-cost trade-off problem. We evaluate this question in a real production moderation setting, with tool-use and GPQA as supporting contrast tasks that expose different output-cost regimes. We compare no-experience baselines, random experience controls, global prompt injection, and retrieval-based selective injection, and analyze both task quality and serving cost. The results show that, once experience becomes case-dependent, selective retrieval provides a stronger operating point than unconditional global injection. They further show that retrieval quality matters more than simply increasing Top-$K$, and that the same serving policy can exhibit substantially different cost-benefit profiles across short-output and decode-heavy regimes. These findings suggest that external experience is best treated as a selective, cost-aware serving decision rather than as a universal add-on. Overall, in the settings studied here, external experience pays off only when both the serving interface and the task-specific cost structure make its quality gains worth the online cost.

翻译：生产级大型语言模型系统会积累可复用的运行经验，但实际部署的核心问题并非仅在于此类经验能否发挥作用，而在于不同服务策略如何在现实约束下平衡质量与在线成本。注入外部经验可提升任务质量，却也会增加提示负担、延迟与服务压力。本文将“外部经验服务”视为一个面向部署的质量-成本权衡问题，在真实生产审核场景中开展评估，并以工具调用与GPQA作为辅助对照任务以揭示不同输出-成本区间特性。我们对比了无经验基线、随机经验控制、全局提示注入及基于检索的选择性注入策略，从任务质量与服务成本两个维度进行分析。结果表明：当经验需根据具体案例定制时，选择性检索比无差别全局注入能提供更优的操作点；检索质量比单纯增加Top-K选取数量更为关键；同一服务策略在短输出与解码密集型场景下会呈现显著不同的成本效益特征。这些发现表明，外部经验应被视为一种选择性、成本感知的服务决策而非通用附加模块。总体而言，在本研究的设定下，仅当服务接口与任务特定成本结构能使其质量收益值得付出在线成本时，外部经验才具有实际价值。