RAG, embeddings, vectors, governance
RAG grounds an LLM in your data: embed → retrieve → augment prompt → generate. Quality is dominated by retrieval, not the model.
Embeddings turn text into vectors; ANN indexes find nearest neighbors fast. Pick the model for your language and domain, and benchmark recall — defaults are rarely best.
Production LLM features need input/output filtering, audit logs, PII handling, and a clear human-override path. The model is the easy part.