Narrowcontext

Narrowcontext is a term used in information processing to describe a limited or local set of contextual information used to interpret input. In contrast to broad or global context, narrowcontext confines interpretation to a small, domain-specific window such as recent dialogue turns, a fixed document segment, or a curated knowledge subset.

In natural language processing, narrowcontext refers to constraining the model's attention to a short context window, or to a restricted knowledge base during reasoning. This approach can improve efficiency, reduce computational requirements, and protect privacy by avoiding exposure to unrelated data. Techniques include limiting the token window in sequence models, using domain-specific embeddings, and retrieval-augmented generation that queries a focused corpus rather than the entire dataset.

Applications include enterprise chatbots that operate within a fixed product domain, on-device assistants with local data, and customer support systems where only the current ticket and relevant policy documents are consulted.

Limitations include loss of long-range dependencies, struggles with cross-domain references, and potential inconsistencies when context shifts. It can be sensitive to the selection of the context window and data used.

Narrowcontext is related to concepts such as context window size, local versus global context, and the distinction from broad-domain or general AI. Some researchers view narrowcontext as a design principle in privacy-preserving or real-time systems.

See also: context window, domain adaptation, retrieval-augmented generation.