For those who don't have 65 minutes, if you write software you are probably familiar with the concepts of evals, caching, guardrails, defensive UX, and collecti

For those who don't have 65 minutes, if you write software you are probably familiar with the concepts of evals, caching, guardrails, defensive UX, and collecting user feedback, none of which are really unique to LLMs. The other two items are "fine-tuning" which just means nudging the LLM to be better at responding a certain way, and "RAG" which is a new acronym that just means using the input to look things up in a database first and concatenate them into the prompt so the LLM uses it as part of the context for token generation.