ai-engineering

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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