RAGmediated

RAGmediated is a term used in discussions of AI systems that rely on retrieval-augmented generation (RAG) as a mediating layer between user queries and generated content. In a RAGmediated architecture, a retriever searches an external knowledge base to locate relevant passages, which are then used by a generator to produce an answer. The retrieval step acts as a mediator by filtering, ranking, and grounding the information before it influences the output. This mediation can also encompass constraints on source type, citation requirements, or policy-driven filtering to steer the final response.

Core components typically include a retriever (dense or sparse, indexing a corpus or knowledge graph), a generator (often a Transformer-based model that composes fluent text conditioned on retrieved passages), and a mediation module or policy layer that governs source selection, attribution, and factual constraints. The resulting system aims to combine the strengths of rapid, context-aware retrieval with the flexibility of generative text, while maintaining provenance for the information used.

Applications span enterprise knowledge management, customer support, research assistance, and content generation with auditable citations. RAGmediated systems are valued for improved factual grounding, the ability to incorporate current information, and enhanced traceability of outputs to their sources. Challenges include dependence on the quality and coverage of the external corpus, potential latency, alignment between retrieved passages and generated text, and the need to manage bias within sources. Evaluation typically considers factuality, source reliability, citation quality, and response efficiency. See also Retrieval-Augmented Generation and knowledge-grounded dialogue.