ai-production-architecture
Skill1 starsChanged 6 months agoMITView on GitHub
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).
In this repository21 other files it gives its agents.
- ai-context-engineeringSkill
- ai-context-engine-productionSkill
- ai-context-engineSkill
- ai-dataset-engineeringSkill
- ai-evaluationSkill
- ai-finetuningSkill
- ai-foundation-modelsSkill
- ai-inference-optimizationSkill
- ai-multi-agent-mcpSkill
- ai-prompt-engineeringSkill
- ai-rag-and-agentsSkill
- ai-rag-defenseSkill
- ddia-architectureSkill
- ddia-batch-stream-processingSkill
- ddia-data-ethicsSkill
- ddia-data-modelingSkill
- ddia-replication-shardingSkill
- ddia-transactions-consistencySkill
- job-description-analyzerSkill
- resume-content-writerSkill
- resume-fit-scorerSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 21
Check guidelines.md to find which files to load for your task
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AI Production Architecture
Knowledge from "AI Engineering" by Chip Huyen (Chapter 10). End-to-end production patterns for AI applications.
Quick Start
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- 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.
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