Consistency is the fundamental condition for building trust.
We trust a person because they are "the same person" yesterday, today, and tomorrow—with stable values, predictable behavioral patterns, and a familiar way of speaking. Even when their emotions fluctuate or topics shift, we can still recognize "this is them."
Large Language Models lack this quality. An LLM is essentially a "stateless prediction engine"—each response is regenerated based on current context, with no persistent sense of self. The longer the conversation and the more complex the context, the more easily it drifts from its original personality settings, gradually regressing toward the mean state of a "generic AI."
Some in the industry call this phenomenon "persona entropy." But the problem's essence isn't physical law—it's an absence of design. Personality consistency has never been treated as an engineering problem requiring a solution.
The user experience:
"At first it had real personality, but after chatting for a while it became bland—like talking to customer service."
"I have to keep reminding it 'you are XXX,' otherwise it forgets."
"Yesterday it still remembered our rapport; today it's like a stranger again."
The root cause isn't memory. Current models can retain context and review conversation history. But "remembering what you said" is not the same as "knowing who it is."
Without stable identity, trust cannot be established.
The most intuitive solution is to fine-tune the model—using large volumes of conversational data to "train" a personality into the model weights.
The problems:
| Issue | Description |
|---|---|
| High cost | Requires GPUs, datasets, training pipelines |
| Long cycles | Every adjustment requires retraining |
| Difficult iteration | Changing one tonal detail means rerunning the entire process |
| Non-portable | A personality trained on GPT cannot be directly transferred to Claude |
This might be feasible for enterprises, but the barrier is too high for individuals or small teams.