seekbased

Seekbased is a term used in information retrieval and artificial intelligence to describe systems and workflows that base outputs on an active search process rather than solely on pre-existing stored knowledge. The concept emphasizes the role of seeking and exploring external sources to gather evidence, verify claims, and fill gaps in understanding.

Overview

Seekbased approaches integrate search as a central component of reasoning. They typically begin with an initial query, retrieve relevant documents or data from indexed sources or live databases, and then synthesize an answer while possibly refining the query and repeating the retrieval loop. This contrasts with purely memory-based or static-model approaches that rely primarily on pre-trained parameters.

Mechanism

In a seekbased workflow, the system formulates searches, evaluates retrieved results for relevance and credibility, and aggregates information to produce an answer with traceable provenance. Techniques often involve document ranking, evidence scoring, and citation management. Some implementations employ iterative refinement, where the output prompts further searches to improve accuracy or update outdated information.

Applications

Seekbased methods are applied in open-domain question answering, knowledge-augmented dialogue, research assistance, and compliance or finance tools where up-to-date or source-backed information is critical. They support transparency by providing sources and allow users to verify the basis of conclusions.

Advantages and limitations

Advantages include improved up-to-dateness, verifiability, and reduced reliance on a fixed knowledge cutoff. Limitations involve latency from external queries, dependence on source quality, potential exposure to unreliable information, and increased complexity in system design and evaluation.

Related concepts include retrieval-augmented generation and open-domain question answering.