Episode Details

A language model has no memory of your character. Everything — voice, history, how they react to being interrupted — has to be re-injected into a finite context window every single turn. So character consistency isn't a personality problem; it's a budget problem. This episode takes apart the retrieval machinery a hobbyist community built to solve it: keyword matching with regex and scan depth, vector storage that fires on semantic similarity, secondary keys with boolean logic, recursion with caps and safety valves, inclusion groups for variety, and insertion positioning that changes how strongly a model weights the same text. Along the way: why the docs openly admit semantic matching is unpredictable, and the honest answer to whether any of this has actually shown up in mainstream AI products. Episode #531488 — open it directly at myweirdprompts.com/531488