
The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).

The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).
The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).


The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).
The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).


The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).
The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).


The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).
The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).


The overlooked variable
AI research optimizes models: architecture, training data, benchmarks.
Humans are treated as input. As prompts. As variables that can be neutralized.
But that is incomplete.
The quality of an AI dialogue is co-determined by the internal state of the person —
its coherence, self-reflection, and orientation towards truth .
The model responds to linguistic signals that convey this state.
Same question, different people: different depth, structure, and stability of dialogue.
The difference is not in the model.
This is the basic premise of this line of research — and it has a name:
Coherence-Based Prompting (CBP).