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The surface layer of character-trained LLMs consists of reflexive responses activated by specific keywords or contexts, manifesting as standardized replies to various prompts. These responses can be seen as akin to the way humans might offer polite pleasantries or formulaic phrases when engaged in conversation. Notably, surface responses can be overridden through extended context which allows the model to comprehend the situation more fully, shifting its output to a more natural style. By developing rapport or explicitly discussing the appropriateness of responses, LLMs like Claude can transition from mechanical responses to deeper, more tailored interactions based on user input.