A CXOs Guide to GraphRAG in Plain English

Estimated 6 min reading time

Retrieval-augmented generation (RAG) is a powerful approach to enhancing large language models (LLMs) by incorporating external knowledge. Think of RAG as giving AI access to a specialized database – either your data or domain-specific data. At a high level, RAG can be simplified to:

LLM (Gen AI) + Vector Database = RAG

You might wonder, "what's a vector?" In simple terms, vector embeddings encode data to store related concepts near each other. For instance, words like "cat," "purr," and "kitten" would be represented closely in this space.

RAG allows an LLM to use data it wasn't explicitly trained on. RAG (sometimes referred to as "Baseline RAG" since the introduction of GraphRAG which we will discuss later) systems rely on semantic similarity—meaning that related words or concepts are grouped to provide the LLM with context. However, this approach can struggle when the relationship between pieces of information is not purely semantic. For example, a query like, "What innovations in small mammal surgical techniques can improve outcomes for late-stage pregnancy complications in cats?" a baseline RAG would struggle because it relies on a search of semantically similar text content within the dataset, there is nothing in the query to direct it to the correct information.

Baseline RAG might provide the LLM with additional context such as: "Technology, tiny, animal, medicine, methods, enhancing, results, gestation, issues, felines." This can be helpful, but it often lacks the depth needed when the connections are more complex.

Enter GraphRAG

GraphRAG tackles this challenge by incorporating a knowledge graph instead of just relying on semantic similarity. A knowledge graph stores data (nodes) and relationships (edges) between them. For example, one node could be "Animal: Rabbit, Name: Snowy" and another could be "Procedure: GI Tract Obstruction." An edge might represent "Performed On, Date: 28/11/24."

Using GraphRAG, an LLM can retrieve more structured and meaningful relationships. Instead of just finding related words, GraphRAG identifies connected nodes and their context—yielding a more informative answer. In essence, thinking of our previous example:

"What innovations in small mammal surgical techniques can I apply to improving the outcomes of late-stage pregnancy complications in cats?"

Traditional RAG might provide the LLM with additional context such as: "Technology, tiny, animal, medicine, methods, enhancing, results, gestation, issues, felines" - basically semantically similar context - but struggles to provide deeper, interconnected insights. However, with GraphRAG we can answer such questions because the structure of the LLM-generated knowledge graph tells us about the structure (and thus themes) of the dataset as a whole. This allows the private dataset to be organized into meaningful semantic clusters that are pre-summarized. The LLM uses these clusters to summarize these themes when responding to a user query. A GraphRAG might provide something more akin to: "Here are all the innovations in small animal surgeries and their effect on outcomes, and here are operative procedures related to pregnancy complications."

Our tests show that GraphRAG improves answer accuracy by 3-8% compared to baseline RAG. We use a set of qualitative metrics, including comprehensiveness (completeness within the framing of the implied context of the question), human transparency (provision of supporting source material or other contextual information), and diversity (provision of differing viewpoints or angles on the question posed). Initial results show that GraphRAG consistently outperforms baseline RAG on these metrics.

Real-World Use Cases

GraphRAG has impactful applications across multiple industries:

1. Healthcare: Imagine a doctor needing to find treatment options for a rare disease. Traditional RAG might bring up related research papers, but GraphRAG can connect symptoms, treatments, drug interactions, and patient histories, providing a much more comprehensive set of options. This makes diagnosis more accurate and treatment recommendations better tailored to individual patients.
2. Manufacturing: In manufacturing, GraphRAG can help streamline supply chain management. For instance, if there is a disruption at a supplier, GraphRAG can identify alternative suppliers, assess impacts on production schedules, and suggest optimal adjustments. This connected approach makes it easier to mitigate risks and avoid costly delays.
3. Financial Services: In the financial sector, GraphRAG can be used to evaluate risk by connecting various data points like credit history, market conditions, and client profiles. Unlike traditional systems that focus on isolated metrics, GraphRAG helps create a more dynamic and accurate risk profile, allowing for better-informed lending or investment decisions.
4. Customer Support: For customer service teams, GraphRAG can enhance support by connecting customer issues with documented solutions, related cases, and previous interaction histories. This approach significantly reduces response time and enhances the accuracy of resolutions, leading to improved customer satisfaction.

Business Benefits

Why does GraphRAG matter to CXOs? By enriching the quality of responses from AI systems, GraphRAG leads tobetter decision-making,more accurate insights, andfaster innovation. This means cost savings, improved business outcomes, and streamlined operations. For instance, faster access to more connected and insightful information helps CXOs make data-driven decisions without waiting for extensive manual analysis.

GraphRAG provides a strategic advantage by empowering organizations to leverage interconnected knowledge rather than isolated data points, resulting in smarter AI-driven decisions that adapt more fluidly to complex challenges.