How do you turn a jumble of vector embeddings into clear, human-readable answers? In this post, we walk through the journey from raw embeddings to precise, context-aware responses—bridging the gap between data and understanding.
When working with embeddings in PostgreSQL, particularly for use cases like semantic search, recommendation systems, or retrieval-augmented generation (RAG), how you prepare and ingest data matters just as much as how you query it.
