Prompt context and resource context serve different roles in how systems process information. Prompt context refers to the specific input or instructions given directly to a system (like a language model) to guide its immediate response. It includes the text, parameters, or data explicitly provided in the request. For example, when asking a chatbot, “Summarize this article: [text here],” the article text and the word “summarize” form the prompt context. This context is temporary, existing only for the duration of the request, and is used to generate a relevant output. In contrast, resource context refers to the broader, persistent data or environment that a system can access or reference beyond the immediate input. This might include databases, APIs, user profiles, or configuration files that provide additional information needed to fulfill a request. For instance, a weather app might use a user’s saved location (stored in a database) as part of its resource context to fetch current weather data, even if the user’s prompt is simply “What’s the weather today?”
A key difference lies in their scope and lifespan. Prompt context is narrow and tied to a single interaction, while resource context is wider and reusable across multiple requests. For example, a developer building a customer support bot might design the prompt context to include the user’s current question (“How do I reset my password?”), while the resource context could pull the user’s account details from a backend system to personalize the response. The prompt context tells the bot what to focus on now, while the resource context provides the tools or data needed to answer effectively. Another example is a code-generation tool: the prompt context might be a user’s instruction (“Write a Python function to calculate factorial”), while the resource context could include libraries, coding standards, or existing code snippets the tool is allowed to reference.
These concepts often work together. A system might combine prompt context (the immediate input) with resource context (external data) to produce a complete response. For instance, when using a language model to answer questions about internal company policies, the prompt context would be the user’s query (“What’s the vacation policy?”), and the resource context might be a vector database of policy documents the model searches through. Developers must manage both: ensuring the prompt context is clear and specific, while resource context is efficiently accessible and secure. Misunderstanding these can lead to issues—like a chatbot leaking sensitive data because it incorrectly included restricted resource context in its prompt, or a model providing generic answers because resource context wasn’t properly integrated. Clarity in separating these layers improves both performance and security.