先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
为客服问答设计RAG方案，说明分块、混合检索、引用校验与租户隔离的缓存键。
输出
预期交付：架构方案草图、代码示例、验证要点与风险边界清单。（编辑说明，非实际运行结果）
方法说明
--- name: llm-app-patterns description: "Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries." risk: critical source: community date_added: "2026-02-27" --- # 🤖 LLM Application Patterns > Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries. ## Detailed Guide Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely. ## When to Use This Skill Use this skill when: - Designing LLM-powered applications - Implementing RAG (Retrieval-Augmented Generation) - Building AI agents with tools - Setting up LLMOps monitoring - Choosing between agent architectures --- ## Inputs, worked example and verification Record provider/SDK versions, authorized data and tools, request/response schemas, latency/cost budgets and the expected task outcome. All code above is an integration sketch: `llm`, database, parser and provider-response adapters are project-owned and must be implemented explicitly. Never execute model-provided Python, expressions or fuzzy tool names; dispatch only exact registered tools after schema and authorization checks. Enforce per-call deadlines as well as loop limits. Example: a planner first queues steps A and B, then after A replaces the remaining work with C. The executor must run A then C, never stale B. A parser step must apply the parser to the actual prior output. Two tenants with the same prompt must produce different cache keys, and cache reads/writes require an explicit reuse policy. These are concrete regression assertions; successful mocks still do not prove live provider behavior. The limiter sketch is single-threaded and process-local. A distributed deployment needs a shared atomic limit and deadlines. Retry only retry-safe operations, honoring provider retry guidance; a timeout after a side effect is not evidence that nothing happened. See [Tenacity retry predicates](https://tenacity.readthedocs.io/en/latest/). ## Limitations - A retrieved source list does not prove each answer claim is supported; verify claim-to-source evidence and abstention behavior. - Prompt text and JSON-shaped output are not authorization boundaries. Enforce permissions in the application. - Provider failover can change output quality, tool schemas, cost and data residency; only use pre-approved compatible fallbacks. - Caching must respect tenant access, data/prompt revisions and deletion policy; temperature zero does not make output deterministic. - Model/SDK examples are not a complete service, benchmark or production-readiness certificate.