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).

ai-engineering

AI Engineering Skills

AI Engineering Stack

12 Core Skills

Development Process

Quick Decision Guide

Reference