Embedding pipelines for RAG systems are fundamentally a data engineering problem, not a new AI discipline. The three core stages map directly to ETL: ingestion (extraction) requires change data capture to keep documents fresh; chunking (transformation) must be treated as a versioned pipeline parameter tuned to content type; and indexing (load) demands that every chunk be tagged with the embedding model version to avoid silent retrieval degradation when models are upgraded. Production reliability requires the same observability practices as any data pipeline: chunk count health checks, golden query sets for regression testing, lineage tracking, freshness monitoring, and retrieval quality SLAs. Teams that treat the data layer as an afterthought end up with AI systems that quietly return wrong answers.
