Large language models can produce inaccurate or misleading outputs, often called hallucinations, especially when lacking context or alignment with specific business needs. Integrating them with knowledge graphs offers a solution by grounding AI responses in structured, connected data that provides context, relationships, and meaning. This combination enhances accuracy, improves reasoning, and enables more reliable insights. By linking semantic understanding with generative capabilities, organizations can better align AI with real-world data, unlocking more effective knowledge extraction, decision-making, and advanced intelligence applications across various domains.

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