AI · 1h ago
RAG Explained: How to Ground LLMs in Your Own Data
RAG (Retrieval-Augmented Generation) lets LLMs answer from private knowledge bases by retrieving relevant chunks and injecting them into the prompt. The pipeline involves embedding questions, searching a vector DB, and augmenting the prompt with context. Without RAG, models hallucinate; with it, answers are accurate and citable.
Meridian48 take
A clear, practical primer on RAG, but it glosses over the fragility of retrieval—bad search still yields bad answers, which is why reranking and corrective RAG exist.
ragllm-applications