Contextplease

Contextplease is a term used in discussions of artificial intelligence and human–computer interaction to describe patterns, protocols, or tools that manage and deliver relevant background information to an AI system in order to improve response quality and continuity. At its core, contextplease centers on attaching, sharing, or retrieving background material—such as user goals, prior interactions, related documents, or domain data—to a current query so the AI can reason with appropriate grounding.

Key concepts often associated with contextplease include context capture interfaces, relevance scoring, and automatic summarization. Systems may support selective disclosure of context, user privacy controls, and versioning to track how background material influences responses over time. Depending on the implementation, context can be injected into prompts, used by separate memory or retrieval modules, or combined with live user input to guide the AI’s reasoning.

Common use cases span customer support bots that recall previous tickets, coding assistants that reference project documentation, and research tools that incorporate relevant literature. The approach is compatible with various architectures, including chat-based assistants and task-oriented agents, and can complement prompt engineering and external memory techniques.

Historically, contextplease emerged as researchers and developers sought methods to reduce hallucinations and improve reliability as models gained larger context windows. While praised for enhancing continuity and relevance, it raises considerations around privacy, consent, data minimization, and transparency about how context influences responses.