ai-data-engineering

SKILL.md

Build data infrastructure for AI/ML systems including RAG pipelines, feature stores, and embedding generation. Provides architecture patterns, orchestration workflows, and evaluation metrics for production AI applications.

RAG pipelines have 5 distinct stages. Understanding this architecture is critical for production implementations.

Chunking is the most critical decision for RAG quality. Poor chunking breaks retrieval.

AI/ML 系统的数据管道、特征存储和嵌入生成。在构建 RAG 管道、ML 功能服务或数据转换时使用。涵盖特征存储（Feast、Tecton）、嵌入管道、分块策略、编排（Dagster、Prefect、Airflow）、dbt 转换、数据版本控制（LakeFS）​​和实验跟踪（MLflow、W&B）。 来源：ancoleman/ai-design-components。

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