Unifying LLMs & Knowledge Graphs for GenAI: Use Cases & Best Practices

The text discusses the integration of Large Language Models (LLMs) and Knowledge Graphs, two powerful technologies that are changing the AI landscape. LLMs, like ChatGPT, can understand and respond to human-like text, while knowledge graphs provide a structured representation of information, making it more intelligent and accurate. The combination of these two technologies is called retrieval augmented generation (RAG), which retrieves relevant information from knowledge graphs using vector and semantic search, then augments the response with contextual data. This process generates precise, accurate, and contextually relevant output. The text also highlights various use cases for this technology combination, including building apps, converting unstructured data to knowledge graphs, creating graph dashboards with natural language queries, generating cyberattack countermeasures, and fine-tuning an open-source LLM for text-to-Cypher translation. Additionally, it provides resources for getting started with Neo4j and LLMs, such as a free course on GraphAcademy and a guide on building a knowledge graph.

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