Should Graphs Power AI Before or After the LLM?

Incorporating graphs into AI systems before and after the use of large language models (LLMs) enhances their reliability and accuracy, as graphs provide structure that LLMs lack. Before LLMs generate responses, graphs improve the retrieval process by ensuring that the AI begins with entity-level grounding, multi-hop context, and verified relationships, forming a structured context that reflects the business domain's reality. After generation, graphs validate the AI's output against authoritative data, checking for nonexistent entities, incorrect relationships, and logical contradictions, which is crucial in high-stakes environments. This dual use of graphs, known as GraphRAG, reduces retrieval uncertainty and mitigates risks associated with LLM-generated hallucinations, making AI systems more stable, grounded, and consistent. TigerGraph, a platform that supports real-time graph traversal and schema-driven modeling, exemplifies this approach, enabling AI systems to operate with enhanced structure and clarity.

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