The era of controlling AI through “clever wording”, symbolized by the term “Prompt Engineering”, is giving way to a more fundamental approach: the “control of structure.”
The act of assigning roles to LLMs is no longer about superficial exchanges of words; it is transforming into a technology that deeply intervenes in the model’s reasoning process itself.
Performing Through Structure
Symbolizing this transformation is the introduction of a dual-layer thinking architecture that mimics human cognitive processes.
This approach treats two distinct layers independently: “system thinking”, which handles the big picture and strategic planning within the model, and “role thinking”, which expresses emotions and intentions as a character in individual interactions.
Through this separation, AI has begun to overcome previous weaknesses, such as losing track of the narrative by becoming too absorbed in playing a specific role.
High-level performance that humans naturally execute—maintaining broad consistency while expressing subtle individuality—is now being reproduced at a structural level.
Self-Adapting Personas
Moreover, the very nature of learning is shifting from labor-intensive data injection to a form where models adapt autonomously.
As exemplified by a technique called Persona-Aware Contrastive Learning, current technology now makes it possible to reinforce consistency within specific roles through contrastive learning processes.
This enables roles with particular personality traits or specialized knowledge backgrounds to be embedded into models with far greater accuracy and at a significantly lower cost than before.
Memory as a Wedge
Furthermore, memory management techniques have undergone dramatic evolution to prevent the loss of self during long-term conversations.
In modern systems, memory-augmented approaches have become standard. These not only trace conversation history but also structurally retain configured personalities and past critical decisions, dynamically retrieving them according to the current context.
By continuously referencing who one is and the context in which one exists, even across thousands of conversational turns, control mechanisms that minimize the occurrence of break-character have become a reality.
Practical Hurdles and the Future of Evolution
However, as these structural approaches become widespread, new challenges are coming to light.
As inference processes become multi-layered and memory retrieval occurs more frequently, computational resource consumption increases, creating practical hurdles such as response latency.
Additionally, a trade-off has been observed: models extremely specialized for specific roles may fail to demonstrate their full capabilities when responding to general questions.
The technological trend is shifting from refining LLMs as single, general-purpose brains to reconstructing them as collections of components meticulously designed for specific purposes.
Role functionality is no longer merely an additional feature; it is being redefined as a core technology that enables AI to integrate into society in a more human-like, more specialized, and more consistent manner.