To enhance the performance of Retrieval-Augmented Generation (RAG) and agentic systems with Large Language Models (LLMs) through the integration of knowledge graphs (KGs), several key techniques are employed. These methods enable more reliable, contextually relevant, and accurate outputs, ensuring that both the LLM and the KG complement each other’s strengths.
One core technique is context expansion using the knowledge graph. In traditional RAG systems, input text is processed to retrieve relevant documents or context. However, integrating a KG adds another layer to this process by identifying relationships and entities within the graph that are related to the input text. This is achieved by entity recognition and linking—a process where entities mentioned in the text are mapped to corresponding entities in the knowledge graph. Once entities are linked, the system can fetch related information from the graph, offering expanded context for LLM responses. By grounding the LLM’s output in these pre-validated, structured relationships, the system ensures deeper insights and contextual coherence.