| 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. |
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).
