Specialist argues that language model personality should be structured across four levels to prevent failures during long interactions.
Controlling the tone of voice in artificial intelligence applications should not be treated as an isolated prompt instruction, but rather as a multi-layered architecture. According to vertical AI specialist Isadora Martin-Dye, founder of Isadora & Co, this approach is necessary so that the model's personality remains consistent during prolonged conversations with real users. The thesis was presented based on production code from various projects, including a wedding venue, a personal AI companion, and a tool aimed at families of missing persons.
The proposed methodology divides brand voice construction into four distinct layers. The first addresses the system's immutable identity, followed by a situational mode that adjusts behavior according to the interaction's context. The third layer uses practical examples to anchor the communication style, while the fourth operates as a deterministic veto applied after text generation, blocking responses that deviate from established parameters.
According to Martin-Dye, the main advantage of this segmentation is clarity in defining responsibilities within the prompt. When all behavioral rules are inserted together and mixed, the model tends to lose coherence over time. Separating into layers allows developers to identify exactly which structure should act at each processing stage, preventing the instruction from breaking down in advanced dialogue turns.
The practical application of this architecture is especially relevant for products serving emotionally sensitive audiences or those heavily reliant on relationship-building. The developer works across a portfolio of four companies spanning hospitality and technology, focusing on software design for audiences that, according to her, other AI products frequently misjudge the tone when attempting to interact.
A multi-layered architecture prevents language models from losing coherence and personality during prolonged conversations. When all behavioral rules are mixed in a single prompt, the model tends to break down over time, whereas layered architecture ensures clear responsibilities at each processing stage.
The four layers are: 1) the system's immutable identity, 2) a situational mode that adjusts behavior to the context, 3) practical examples anchoring the communication style, and 4) a deterministic veto that blocks responses deviating from established parameters after text generation.
This architecture is especially relevant for products serving emotionally sensitive audiences or those heavily reliant on relationship-building, such as personal AI companions, hospitality services, and tools for families of missing persons, where maintaining the correct tone is critical.