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ai-architecture

Provides architectural blueprints for AI applications including gateway, orchestration, routing, and observability via OpenTelemetry/Prometheus.

4|1|Updated Dec 30, 2025
Search Tags:#system design#observability#model routing#llmops#ai architecture#production ai
One-click install
npx skills add https://github.com/doanchienthangdev/omgkit --skill ai-architecture
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-architecture
Source: https://github.com/doanchienthangdev/omgkit/tree/main/plugin/skills/ai-engineering/ai-architecture
Command: npx skills add https://github.com/doanchienthangdev/omgkit --skill ai-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexities of designing, scaling, and deploying production-ready AI applications by providing guidance on architectural patterns, model routing, and observability.

Core Features & Use Cases

  • Reference Architecture: Outlines a layered approach for AI applications (Client, Gateway, Orchestration, Model, Data).
  • Model Routing: Implements logic to select the most appropriate AI model based on query complexity and cost constraints.
  • Context Enhancement: Details strategies for retrieving, reranking, and compressing relevant information to enrich AI prompts.
  • Observability: Integrates with tracing and metrics systems (OpenTelemetry, Prometheus) for monitoring AI service performance.
  • Use Case: When building a new AI-powered feature, use this Skill to understand best practices for structuring the application, managing model costs, and ensuring reliable performance.

Quick Start

Use the ai-architecture skill to generate a reference architecture diagram for an AI application.

Frequently Asked Questions about ai-architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to structure production AI application architecture?▼

Production AI application architecture is best structured using a layered approach consisting of Client, Gateway, Orchestration, Model, and Data layers to ensure scalability and reliable performance.

How do I implement model routing to manage LLM costs?▼

Implement model routing by applying logic that dynamically selects the most appropriate AI model based on query complexity and cost constraints, balancing performance and expenses effectively.

How does context enhancement work for enriching AI prompts?▼

Context enhancement works by retrieving, reranking, and compressing relevant information to enrich AI prompts, ensuring the model receives highly targeted data for accurate responses.

Can I use OpenTelemetry and Prometheus for AI observability?▼

Yes, you can integrate OpenTelemetry and Prometheus for AI observability to monitor service performance, capture tracing data, and track metrics across your production infrastructure.

Do I need a gateway layer for scaling AI applications?▼

A gateway layer is needed for scaling AI applications to manage request routing, enforce policies, and handle orchestration before forwarding queries to the appropriate model layer.

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