When AI agents have to execute complex, multi-hop reasoning across interconnected knowledge domains, traditional Retrieval-Augmented Generation (RAG) systems fail. Although vector-based retrieval is very good at finding content that is semantically similar, it is unable to find meaningful connections between related data points, which leads to fragmented insights. In this talk, we will dive into how engineering teams can use GraphRAG and knowledge graphs to ground agents and large language models (LLMs) to uncover connections in enterprise data that are often missed by conventional RAG techniques.
Attendees will learn how this improved architecture makes accurate, explainable, and reliable answers to complex business problems, possible.
Nyah Macklin is a seasoned researcher and speaker on topics around AI, ML, Ethics, Governance, and Responsibility.
Nyah serves as a Senior Developer Advocate for Artificial Intelligence at Neo4j, specializing in GraphRAG, knowledge graphs, and AI-driven developer tooling, where they have built high-impact technical communities and led initiatives that advance a critical understanding of AI and its use cases.
They are also the Founder & CTO of Afros in AI, a technical community dedicated to showcasing the multifaceted nature of artificial intelligence.
Beyond Nyah's technical expertise, they have a background in government leadership and technology policy, having served as Chief of Staff in the U.S. state government, where they helped shape tech-driven legislative initiatives and equity-driven legislation.
When not immersed in their work, Nyah cares about empowering, teaching, and tutoring engineers, live-streaming technical deep dives, and building open-source tools that make software more accessible, explainable, and community-driven.