What is RAG? Why does it exist and what problems does it solve?

RAG (Retrieval-Augmented Generation) solves an LLM's static training cutoff, hallucination on domain-specific facts, and lack of access to proprietary data — it embeds the query, does a vector similarity search, injects the top-k matching chunks into the prompt, and the LLM answers grounded in that retrieved context.