Documentation
README
AI Data Engineering
Purpose
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.
When to Use
Use this skill when:
- Building RAG (Retrieval-Augmented Generation) pipelines
- Implementing semantic search or vector databases
- Setting up ML feature stores for real-time serving
- Creating embedding generation pipelines
- Evaluating RAG quality with RAGAS metrics
- Orchestrating data workflows for AI systems
- Integrating with frontend skills (ai-chat, search-filter)
This is the opening of the README. Read the full README on GitHub.