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Published on 07/05/2024
Last updated on 07/17/2024

Combining retrieval augmented generation with knowledge graphs for more reliable AI analytics


The growth of large language models (LLMs) has transformed how organizations approach data analytics. These models, including applications like Meta’s Llama or Open AI’s ChatGPT, now use billions of parameters, giving them the impressive ability to generalize knowledge and effectively support a variety of enterprise use cases.

However, LLMs may underperform on certain tasks, particularly those requiring time-sensitive or specialized expertise. Because model training is finite, an LLM’s knowledge is limited to what it learned during its foundational training period. As this information becomes outdated, models with knowledge gaps may generate inaccurate responses known as hallucinations. Unreliable outputs are especially common when users prompt models about current events or quickly evolving subject areas, such as technology or medicine. 

To overcome this limitation, companies need a strategy. They could build a new model or fine-tune an existing one, but these are time-,  resource-, and cost-intensive processes. Another approach is to leverage retrieval augmented generation (RAG). This technique ensures an LLM’s analytical capabilities remain accurate and up-to-date without the computational demands of model training.

While RAG is becoming more widespread in LLM development, its performance depends on its underlying data architecture—the structure used for information storage and retrieval. When built using knowledge graphs, RAG can greatly enhance the reliability, accuracy, and transparency of LLM outputs. 

How RAG works 

RAG enables an LLM to access and use new context and information not included in its initial training data. The LLM can retrieve data from external sources and combine it with existing knowledge to generate outputs.

To implement RAG, developers create a knowledge base housing more current or specialized information. This knowledge base is separate from the core training data, but the model can easily retrieve information from it when responding to user prompts.

Practitioners can use RAG and supplemental knowledge bases to prepare LLMs for tasks a foundational model may be unable to handle—like providing technical support for a niche product—without the expense and effort of fine-tuning or retraining. RAG is also an effective way to avoid hallucinations and errors. AI research in the medical field suggests that RAG significantly improves output accuracy over non-RAG models and human experts. 

How knowledge graphs work 

Knowledge graphs, also called semantic networks, map meaningful relationships between entities or pieces of data in a graph-like format. Developers build knowledge graphs using nodes and edges. A node represents an entity, such as an event, person, concept, object, or situation, while edges connect and describe the relationships between each entity.  

The knowledge graph’s structured approach helps various systems—such as search engines and chatbots—navigate, retrieve, and understand context within a dataset more easily. This type of data architecture is often used to improve AI performance because it gives models the in-depth context they need to perform more advanced reasoning. Knowledge graphs also support techniques like ​transfer learning, which are used to accelerate AI development and adoption

How RAG using knowledge graphs supports AI analytics 

Using knowledge graphs with RAG for their LLMs can give enterprises a competitive advantage because the technologies operate in complementary ways. For instance: 

  • LLMs can be indecisive and hallucinate in response to tasks where they lack expertise. Knowledge graphs address this limitation because they’re designed only to capture structured, logical, and factual information rather than improvising to address gaps. 
  • Knowledge graphs often struggle to interpret unstructured text and natural language; a skill LLMs perform well. 
  • Because knowledge graphs clearly map semantic relationships, AI practitioners can easily interpret how and why certain information is retrieved. This mitigates the “black box” nature of neural networks, which use reasoning processes and algorithms that are often difficult to understand.

These complementary traits enable more reliable and accurate LLM outputs and improved system transparency. Such a combination is ideal for complex analytical tasks, like those used in decision-making or question-answer systems.

According to AI research, knowledge graphs and RAG can significantly improve the performance of existing models applied to tasks using AI and predictive analytics. For example, one study found autonomous vehicle systems using LLMs are better able to anticipate pedestrian behaviors when supported by a combination of knowledge graphs and RAG. Other researchers discovered that the approach reduced resolution times by 28.6% in LinkedIn’s technical support systems

Ensuring success with knowledge graphs and RAG 

Despite the benefits of using knowledge graphs with RAG, the technique can be cumbersome and costly. Knowledge graphs are effective for complex tasks but may perform less efficiently than alternative retrieval methods like vector databases. Additionally, large volumes of data create complex node and edge structures, which can be time-consuming and expensive to build. The approach also requires specialized skills in areas like graph and ontology development.

To integrate RAG and knowledge graphs effectively, your organization can follow some practical steps:

  • Evaluate your use case and validate whether a knowledge graph is the best fit. Other approaches could be more economical if your prompts are straightforward, require fast information retrieval, or demand a lot of data storage. However, if your use case demands high accuracy to support complex analytics, utilize a knowledge graph with RAG. 
  • Allocate sufficient time, expertise, and financial resources. If your RAG application requires a lot of data, understand the costs and timelines needed to create a knowledge graph. Upskill or hire talent experienced in graph development and with domain expertise. 
  • Consider using AI to build knowledge graphs. Use an LLM to automatically extract relevant information from data sources, identify nodes and edges within a dataset, and suggest appropriate graph structures for your application. This helps shorten development timelines, especially if you’re handling large datasets. 

Build trust in AI with knowledge graphs and RAG 

Organizations now use AI analytics to help them make a range of consequential decisions, from choosing investments to writing insurance policies. Because training data—and, therefore, AI outputs—can become outdated quickly, enterprises must ensure tools like LLMs are equipped to leverage the latest domain expertise. This is critical for responsible innovation, avoiding AI-generated errors or biases that could harm businesses, governments, and communities downstream.

More enterprises are adopting RAG to keep LLMs up-to-date and knowledgeable, and for good reason. RAG is less costly than fine-tuning a model or training one from the ground up. When supported with knowledge graphs, RAG also helps improve LLM performance, reliability, and transparency.

While knowledge graphs can be a key differentiator in developing more competitive and trustworthy AI tools, organizations must evaluate their options and build the retrieval architecture best suited to their use case.

Dive deeper into retrieval augmented generation use cases and how the technology boosts LLM functionality. 

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