Prasming

Prasming is a conceptual framework used to describe the process of aligning user intent with system actions through a structured mapping from pragmatic goals to semantic inputs. It emphasizes the integration of context, constraints, and expected outcomes to generate appropriate responses in interactive systems.

Derived as a portmanteau of pragmatic and mapping, the term surfaced in academic discussions in the early 2020s to characterize approaches that bridge natural language understanding with action planning. It is used chiefly in human–computer interaction, conversational AI, and robotics.

Core concepts include elicitation of goals, context modeling, and action synthesis. A typical prasming pipeline involves: capturing user intent and situational context; translating this into a set of actionable commands or policies; and validating outcomes through user feedback or system state checks. The framework stresses that pragmatic constraints (efficiency, safety, user preferences) shape the final behavior.

Applications span conversational agents, assistive technologies, autonomous agents, and interactive workflows. Proponents argue prasming can improve robustness by accounting for limited or noisy input and by separating high-level goals from low-level actions.

Critics note that the approach depends on reliable context representation and clear evaluation criteria, which can be challenging in open-ended domains. As of now, prasming remains a conceptual tool used in comparative studies rather than a standardized methodology.