Three different answers to "how do I make this LLM know what I need?" — and picking the wrong one is one of the most common, most expensive mistakes in applied AI work.
Ask someone how to make an LLM "know" something it doesn't already know, and prompt engineering, RAG, and fine-tuning all get mentioned in the same breath — often as if they were three points on a single dial, from "light touch" to "maximum effort." They aren't. Each one operates on a completely different layer of the system: what text you send the model, what facts you hand it alongside that text, or what its weights actually are. Confusing the three doesn't just waste effort — it routinely sends teams fine-tuning a model to "teach it facts" when a much cheaper, more accurate, more maintainable retrieval system would have solved the actual problem.