AlumniGraph AI: Grounding LLMs with Graph RAG for Career Network Analysis

In the era of Generative AI, static vector databases often fall short in providing deep contextual accuracy. Every developer aiming to build next-generation applications must understand how to leverage graph-structured data to ground LLM outputs.

In this session, we will explore the architecture of integrating Knowledge Graphs (KG) with Large Language Models (LLM) to solve complex semantic reasoning challenges in professional networks. We will move beyond basic vector search and implement a Graph-Powered Retrieval-Augmented Generation (RAG) pipeline using LangGraph and Neo4j to analyze alumni data.

You will learn how to design domain-specific graph schemas, execute complex cypher queries for context retrieval, and mitigate hallucinations by grounding AI responses in factual graph data. This session is designed for those looking to apply academic concepts to real-world infrastructure. You will see a live-coding demonstration showing how to traverse relationships to improve semantic depth, along with lessons learned regarding scalability and query optimization. Join us to bridge the gap between structured knowledge and generative intelligence on NODES 2026.

Видео AlumniGraph AI: Grounding LLMs with Graph RAG for Career Network Analysis канала Burhan Arrasyid