RAG changes what a model knows by retrieving from a live knowledge base, while fine-tuning changes how a model behaves by training it on curated examples. In this post, we compare them in terms of accuracy, cost and citations, and show when to reach for each.
Named Entity Recognition (NER) finds the people, organizations and dates inside unstructured text and stores them as metadata alongside the embeddings. In this post, we explain how that metadata sharpens retrieval and show how Progress Agentic RAG automatically detects entities during ingestion.