AI Data Engineering for RAG

A retrieval-augmented system is only as good as the data pipeline behind it, and that pipeline is a data-engineering problem long before it is a model problem. This field covers the unglamorous work that decides RAG quality: ingesting and cleaning heterogeneous sources, chunking documents so meaning survives the split, choosing and tuning an embedding model, building and maintaining a vector index, and re-indexing as the corpus changes. It also covers the freshness discipline — knowing when stored knowledge has gone stale and re-validating it — which is exactly the problem Alloy’s own freshness pipeline is built to solve.