ai-engineering

AI Engineering Skills

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Install skill "ai-engineering" with this command: npx skills add doanchienthangdev/omgkit/doanchienthangdev-omgkit-ai-engineering

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 ↓
  2. Prompt Engineering → 5. Context (RAG/Agents) → 6. Finetuning (if needed) ↓
  3. 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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