System prompts and exposed prompts are two distinct components that shape how a language model processes inputs and generates outputs. A system prompt is a predefined instruction or set of guidelines embedded by developers to steer the model’s behavior, such as setting its tone, style, or constraints. In contrast, an exposed prompt (often called a user prompt) is the direct input provided by an end user during interaction. The relationship between them is hierarchical: the system prompt establishes the foundational rules, while the exposed prompt operates within that framework. For example, a system prompt might instruct the model to “respond concisely and avoid technical jargon,” and the exposed prompt could be a user asking, “Explain quantum computing.” The model’s answer would then align with both the user’s request and the system’s predefined constraints.
To illustrate, consider a customer service chatbot. The system prompt might include directives like “maintain a polite tone” and “prioritize resolving billing inquiries.” When a user submits an exposed prompt such as, “My bill is incorrect,” the model combines the system’s guidelines (politeness, focus on billing) with the user’s request to generate a helpful response. If the system prompt were changed to “provide detailed technical explanations,” the same exposed prompt might yield a more technical answer, even though the user’s input didn’t change. This demonstrates how system prompts act as a filter or lens, shaping how exposed prompts are interpreted and addressed. Developers often adjust system prompts to refine the model’s behavior without altering the user-facing interface.
From a development perspective, system prompts are typically static and controlled by the engineering team, while exposed prompts are dynamic and user-defined. A key challenge is ensuring the system prompt is robust enough to handle a wide range of exposed prompts without over-constraining the model. For instance, a system prompt that enforces strict content moderation might inadvertently block valid user queries if not carefully calibrated. Conversely, overly vague system prompts can lead to inconsistent responses. Balancing these elements requires testing and iteration—adjusting the system prompt to cover edge cases while allowing exposed prompts to remain flexible. This interplay is critical for creating reliable, user-aligned applications.