| name | ai-engineering |
| description | Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI systems. Use when this capability is needed. |
| metadata | {"author":"doanchienthangdev"} |
AI Engineering Skills
Comprehensive skills for building AI applications with Foundation Models.
AI Engineering Stack
┌─────────────────────────────────────────────────────┐
│ APPLICATION LAYER │
│ Prompt Engineering, RAG, Agents, Guardrails │
├─────────────────────────────────────────────────────┤
│ MODEL LAYER │
│ Model Selection, Finetuning, Evaluation │
├─────────────────────────────────────────────────────┤
│ INFRASTRUCTURE LAYER │
│ Inference Optimization, Caching, Orchestration │
└─────────────────────────────────────────────────────┘
12 Core Skills
| Skill | Description | Guide |
|---|
| Foundation Models | Model architecture, sampling, structured outputs | foundation-models/ |
| Evaluation Methodology | Metrics, AI-as-judge, comparative evaluation | evaluation-methodology/ |
| AI System Evaluation | End-to-end evaluation, benchmarks, model selection | ai-system-evaluation/ |
| Prompt Engineering | System prompts, few-shot, chain-of-thought, defense | prompt-engineering/ |
| RAG Systems | Chunking, embedding, retrieval, reranking | rag-systems/ |
| AI Agents | Tool use, planning strategies, memory systems | ai-agents/ |
| Finetuning | LoRA, QLoRA, PEFT, model merging | finetuning/ |
| Dataset Engineering | Data quality, curation, synthesis, annotation | dataset-engineering/ |
| Inference Optimization | Quantization, batching, caching, speculative decoding | inference-optimization/ |
| AI Architecture | Gateway, routing, observability, deployment | ai-architecture/ |
| Guardrails & Safety | Input/output guards, PII protection, injection defense | guardrails-safety/ |
| User Feedback | Explicit/implicit signals, feedback loops, A/B testing | user-feedback/ |
Development Process
1. Use Case Evaluation → 2. Model Selection → 3. Evaluation Pipeline
↓
4. Prompt Engineering → 5. Context (RAG/Agents) → 6. Finetuning (if needed)
↓
7. Inference Optimization → 8. Deployment → 9. Monitoring & Feedback
Quick Decision Guide
| Need | Start With |
|---|
| Improve output quality | prompt-engineering |
| Add external knowledge | rag-systems |
| Multi-step reasoning | ai-agents |
| Reduce latency/cost | inference-optimization |
| Measure quality | evaluation-methodology |
| Protect system | guardrails-safety |
Reference
Based on "AI Engineering" by Chip Huyen (O'Reilly, 2025).
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