Neo4j: The Definitive Guide

Chapter 13. The Future of Graphs with Generative AI

The AI landscape continues to evolve rapidly. No doubt, by the time you have this book in your hands, the field will have advanced significantly. In this final chapter, you’ll find out why graphs make such a difference and how Neo4j has embraced GenAI, one of the most promising frontiers of innovation.

When ChatGPT made its dramatic entrance in 2022, it captured the world’s imagination. It was primarily used to answer questions but quickly evolved to text summarization and image generation. It was soon clear that GenAI models excel at content creation and pattern recognition—but they struggle with factual accuracy, resulting quite often in unreliable results. When LLMs generate content, they’re predicting what should come next based on the statistical patterns they learned during training. But without being trained on more recent or relevant information, they tend to hallucinate, producing confident “facts” that cannot always be trusted. For any nontrivial business domain, this phenomenon is alarming.

Retrieval-augmented generation (RAG) architectures improve the reliability of GenAI systems by ensuring that LLM responses are based on facts and come from various sources of knowledge. Knowledge graphs represent real-world entities and the complex relationships that exist between them. LLMs can produce less biased and more accurate responses by relying on a knowledge graph as the source of facts.

The rise of GenAI has brought ...