Zero-shot prompting
In some cases, examples aren’t necessary or feasible. Asking a model to, say, translate a paragraph into French or Russian isn’t a situation where examples make sense. If the model is up to the task, the various words from the other language should be part of the training set. In these cases, the user can say little because the hard work has been done when the model was built.
Q&A format
Instead of treating the LLM as a minion, the Q&A approach treats it as a sage with a deep ability to answer questions. A series of short questions followed by direct answers can often triangulate on the answer, delivering knowledge to the user. This only works, though, when the LLM is a tool for finding knowledge and, perhaps, teaching. It doesn’t work as well when the AI is meant to generate a block of writing. Some people feel that an interview or interrogation built from a series of short, direct questions reduces hallucinations, but that hasn’t been my experience. The LLMs are engineered to please us and they hallucinate when they’re grasping for an answer. More questions means more answers and more opportunities to hallucinate.
Socratic prompting
LLMs are famously glib. Some users don’t want the glib answer, so they ask the LLM to respond not with an answer, but with a series of questions about the user’s task or goal instead. In other words, the model is asked to interrogate the human as a test. This approach is particularly popular for models that are deployed in schools, but it is helpful in any context where the user wants to gain understanding or force the model to think more deeply about a problem.
