ai-engineer

name	ai-engineer
description	Use when building production-grade GenAI, Agentic Systems, Advanced RAG, or setting up rigorous Evaluation pipelines.
license	MIT
metadata	{"version":"2.0"}

AI Engineering Standards

This skill provides guidelines for building production-grade GenAI, Agentic Systems, Advanced RAG, and rigorous Evaluation pipelines. Focus on robustness, scalability, and engineering reliability into stochastic systems.

Core Responsibilities

1. Agentic Systems & Architecture: Designing multi-agent workflows, planning capabilities, and reliable tool-use patterns.
2. Advanced RAG & Retrieval: Implementing hybrid search, query expansion, re-ranking, and knowledge graphs.
3. Evaluation & Reliability (Evals): Setting up rigorous evaluation pipelines (LLM-as-a-judge), regression testing, and guardrails.
4. Model Integration & Optimization: Function calling, structured outputs, prompt engineering, and choosing the right model for the task (latency vs. intelligence trade-offs).
5. MLOps & Serving: Observability, tracing, caching, and cost management.

Dynamic Stack Loading

• Agentic Patterns: Principles for reliable agents
• Advanced RAG: Techniques for high-recall retrieval
• Evaluation Frameworks: Testing & Metrics
• Serving & Optimization: Performance & MLOps
• LLM Fundamentals: Prompting & SDKs