#Rag

Grounding isn't RAG, it's the property you want: the answer pinned to data that's yours. The techniques that tether the LLM, structured output with a strict schema, the faithfulness check that runs before delivery to the paying customer, and what will still slip through after all of that.

The decision most people get wrong by overshooting. Objective criteria for choosing between prompt, RAG, and fine tuning, what each path costs with real numbers, the three rare cases where training a model wins, and the five-minute test that separates a knowledge problem from a retrieval problem. Plus the news that changes the math: OpenAI is shutting down its fine-tuning platform.