RAG takes the current limit of LLM and focuses it on specific problems using custom data. It's not exactly magic, it just finds a way to produce something tangi

RAG takes the current limit of LLM and focuses it on specific problems using custom data. It's not exactly magic, it just finds a way to produce something tangible and usable from an otherwise broadly focused model. It's the rage because it's a way to practically _do work_ from LLMs that generally provide wow from conversationally accurate, often factually accurate responses.