How Native RAG differs to Graph RAG

Over the past few days, I’ve been diving into various Retrieval-Augmented Generation (RAG) architectures, trying to understand when each one is best suited for a particular use case. One approach that has particularly fascinated me is Graph RAG.

Graph RAG, as the name suggests, is a specialized approach to RAG that leverages a knowledge graph—a data structure composed of nodes (representing entities) and edges (representing relationships)—to provide deeper, interconnected context for large language models (LLMs). Unlike a “naïve” or “native” RAG system, which primarily retrieves isolated documents, Graph RAG taps into the relational links between pieces of information, enabling multi-hop reasoning and delivering more nuanced insights.

A broad range of knowledge-intensive tasks can benefit from Graph RAG. For example, in legal domains, multiple statutes and case precedents often reference or build on one another, requiring an understanding of how these documents interrelate. Similarly, scientific research papers frequently cite each other, and unraveling these connections can yield deeper, more holistic insights into a given field.

Thanks to its multi-hop reasoning capabilities, Graph RAG offers several notable advantages:

• Handles complex, interrelated queries: By modeling relationships explicitly, it can handle queries involving multiple steps or dependencies.

• Offers deeper contextual insights: Traversing a knowledge graph allows you to understand not just what’s relevant, but also why certain pieces of information are related.

However, as with any technique, Graph RAG comes with its own set of drawbacks:

• Higher setup and maintenance costs: Building and maintaining a knowledge graph requires more upfront effort, from designing the schema/ontology to continuously updating relationships.

• Requires careful graph design: Poorly structured graphs can lead to gaps in retrieval or overly complex traversals.

Ultimately, choosing between a native/naïve RAG, a Graph RAG, or even a hybrid approach depends on the nature of your data and the complexity of user queries. In some scenarios, a query router can direct simpler queries to a native RAG pipeline, while sending more complex, relationship-heavy questions to a Graph RAG. Furthermore, the emerging concept of Agentic RAG—where an agent autonomously decides how best to retrieve and combine information—can also be valuable in automating the selection and orchestration of different RAG strategies