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Most organizations recognize the transformative power of Generative AI but are struggling to confidently adopt solutions as they deal with issues such as hallucinations, security, and data bias. Emerging regulatory frameworks will demand safety, transparency, and explainability, especially for consumer-facing experiences. Combining Knowledge Graphs with LLMs creates trustworthy GenAI experiences that minimize risk and maximize relevancy, specificity, completeness, and transparency. Join us in this session to learn:

● How knowledge graphs and LLM can reduce hallucinations and data bias.

● Essential architectural patterns for building conversational experiences grounded by knowledge graphs.

● Advantages of knowledge graphs for retrieval augmented generation (RAG), including graph-native semantic search using vector indexing.

● Leveraging LLMs to accelerate knowledge graph construction, including schema development, entity extraction, and vector embedding. By including knowledge graphs in your GenAI strategy, you’ll have a robust, trustworthy approach for delivering the next generation of intelligent applications.

Katie Roberts, PhD

Data Science Solution Architect, Neo4j