ai-system-templates

name	ai-system-templates
description	Use when a user is designing AI-native systems such as AI chat products, AI gateways, RAG knowledge bases, agent or workflow platforms, inference serving, or vector databases and wants architecture choices and trade-offs grounded in the awesome-architecture templates.

AI System Templates

Use the awesome-architecture AI-native templates as architecture maps for LLM-era systems. These templates focus on system shape, bottlenecks, guardrails, and trade-offs rather than framework selection.

Source Of Truth

• Local source repo: ~/.hermes/external-repos/awesome-architecture
• Template map: references/template-map.md

When To Use

• AI 架构, AI 聊天架构, AI 网关
• RAG 架构, 向量数据库架构
• AI Agent 平台架构, AI 工作流架构
• 模型推理服务架构, inference serving

For classic product architectures, route to comm/arch/general-system-templates. For method and trade-off framing, route to comm/arch/architecture-thinking.

Workflow

1. Map the request to one or two AI-native templates from references/template-map.md.
2. Read the matching upstream template files before answering.
3. Structure the answer as:
   • problem shape
   • core runtime components
   • retrieval / memory / serving path
   • cost, latency, and reliability pressure points
   • guardrails and control points
   • evolution path
4. If the system combines agentic behavior with retrieval or gateway concerns, explicitly separate control plane from data plane.

Guardrails

• Keep the answer architecture-first; avoid jumping straight to framework picks.
• Distinguish retrieval, orchestration, inference, and storage responsibilities.
• Make cost/latency/quality trade-offs explicit.

References

• references/template-map.md