Context Provisioning
Context provisioning is a method of supplying information to AI agents that pre-organizes content into structured, hierarchical formats before presentation. Unlike traditional retrieval-augmented generation (RAG) systems that rely on vector similarity search to locate relevant passages within documents, context provisioning uses a map-first architecture to establish relationships and hierarchy within the information space. This approach reduces the agent’s need to search through unstructured data and instead provides a cognitively organized representation of available knowledge.
Comparison to RAG Systems
Traditional RAG workflows typically involve uploading documents, converting them to embeddings, and retrieving passages based on semantic similarity to a query. Context provisioning diverges from this pattern by requiring upfront structuring of information into organized maps or knowledge graphs. This preparation phase aims to address documented limitations of RAG systems, including context fragmentation, difficulty with hierarchical reasoning, and reduced performance on tasks requiring integrated understanding across multiple information sources.
Implementation and Use Cases
Context provisioning systems typically organize information as maps, outlines, or graph structures that explicitly encode relationships between concepts. When an agent requires context, it receives a pre-filtered, hierarchically organized subset of information rather than performing vector searches. This architecture is particularly suited for agents working with complex domains, large knowledge bases, or tasks requiring multi-step reasoning that depends on understanding how different pieces of information relate to one another.