All right, here is a theory: LLMs contain "latent knowledge" that is sometimes used by the model during inference, and sometimes it isn't. One way to "engage"

All right, here is a theory: LLMs contain "latent knowledge" that is sometimes used by the model during inference, and sometimes it isn't. One way to "engage" these internal representations is to include keywords or patterns of text that make that latent knowledge more likely to "activate". Say, if you want to ask about palm trees, include a paragraph talking about a species of a palm tree (no matter whether it contains any information pertaining to the actual query, so long it's "thematically" right) to make a higher quality completion more likely. It might not be the actual truth or what's going on inside the model. But it works quite consistently when applied to prompt engineering, and produces visibly improved results.