Knowledge Runtime
Knowledge Runtime refers to the operational layer within an Artificial Intelligence (AI) application that manages the retrieval, contextualization, and application of external, proprietary, or real-time knowledge sources. It is the mechanism that allows a large language model (LLM) or AI agent to move beyond its static training data and interact with current, specific enterprise information.
In enterprise settings, general-purpose LLMs often lack domain-specific knowledge or up-to-date information. Knowledge Runtime solves this by grounding the AI's responses in verified, internal data. This drastically reduces hallucinations, improves factual accuracy, and enables the AI to operate as a true subject matter expert for the organization.
The process typically involves Retrieval-Augmented Generation (RAG). When a user submits a query, the Knowledge Runtime first parses the intent. It then queries a vector database or knowledge graph containing the organization's documents. Relevant snippets of text are retrieved, and these snippets are injected directly into the LLM's prompt as context. The LLM then generates an answer based only on this provided context.
This concept is closely related to Vector Databases, Embeddings, Prompt Engineering, and Retrieval-Augmented Generation (RAG).
