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The GraphRAG Manifesto: Adding Knowledge to GenAI

neo4j.com · 5,690 words · saved by 2 readers

Yes. Techniques like vector-based RAG and fine-tuning can help. And they are good enough for some use cases. But there’s another whole class of use cases where these techniques all bump into a ceiling. Vector-based RAG – in the same way as fine-tuning – increases the probability of a correct answer for many kinds of questions. However neither technique provides the certainty of a correct answer. Oftentimes they also lack context, color, and a connection to what you know to be true. Further, these tools don’t leave you with many clues about why they made a particular decision. Back in 2012, Google introduced their second-generation search engine with an iconic blog post titled “Introducing the Knowledge Graph: things, not strings1.” They discovered that a huge leap in capability is possible if you use a knowledge graph to organize the things represented by the strings in all these web pages, in addition to also doing all of the string processing. We are seeing this same pattern unfold i

The GraphRAG manifesto: Adding knowledge to GenAI Skip to content Neo4j to acquire GraphAware, launch new open-standards intelligence analysis solutions | Read more Menu Search Close Menu Products FULLY-MANAGED AuraDB Store and query connected data at scale Virtual Graph Create and query a knowledge graph on existing data Aura Graph Analytics Run graph algorithms on any data, any cloud Aura Agent Build and deploy context-aware agents fast SELF-MANAGED Graph Database Store connected data with a graph database Graph Data Science Run graph algorithms on connected data Bloom Securely query, explor

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