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AI Production Architecture

Practical knowledge for architecting and operating AI applications in production. Covers the AI engineering architecture (context enhancement, guardrails, model router, gateway, caching, agent patterns), monitoring and observability (metrics, logs, traces, drift detection), pipeline orchestration, and user feedback systems (extracting conversational feedback, feedback design, biases, degenerate loops). Use this skill when: - Designing the architecture for a production AI application - Adding input/output guardrails - Setting up model routing or a gateway - Implementing caching (exact, semantic) - Building observability for an AI system - Designing user feedback collection

ebarti Updated
DevOps & Infra

File contents

AI Production Architecture

Knowledge from "AI Engineering" by Chip Huyen (Chapter 10). End-to-end production patterns for AI applications.

Quick Start

  1. Check guidelines.md to find which files to load for your task
  2. Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
  3. Apply guidance to your work

Contents

References

Category Purpose
architecture-patterns Step-by-step architecture (context, guardrails, router/gateway, caching, agents)
monitoring-observability Metrics, logs/traces, drift detection, pipeline orchestration
user-feedback Extracting conversational feedback, feedback design, biases, degenerate loops

Workflows

Task Workflow
Build production AI architecture (5-step process) workflows/build-production-architecture.md
Set up observability (metrics, logs, traces, drift) workflows/setup-observability.md

Guidelines

See guidelines.md for task-based file selection.

ebarti/skills/tree/main/ai-production-architecture commit f209364a94

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Frequently asked questions

npx skillmds@latest add ebarti/ai-production-architecture