RAG bridges the gap between an LLM's training data and your specific knowledge. The idea: when the user asks a question, retrieve the most relevant chunks from a vector database, paste them into the prompt, then let the model answer with that context. RAG is the default architecture for support bots, internal Q&A agents, and anything that needs to answer from a corpus of documents you control.
Engineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.