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