Knowledge Graphs Improve Gen AI: Validating Results Builds Trust for Organizations

Generative AI (GenAI) offers transformative potential for organizational decision-making, but trust in its outputs is crucial. Knowledge graphs play a vital role in ensuring the accuracy, reliability, and effectiveness of GenAI, addressing critical shortcomings related to unstructured data and lack of transparency.

The Challenge of Trusting GenAI

GenAI relies on Large Language Models (LLMs) that draw upon millions of data points, making it difficult to analyze how answers are derived. This poses challenges:

• Verifying accuracy: If different GenAI tools provide different answers to the same question, how can organizations determine the correct result?
• Fact-checking responses: The unstructured nature of data in LLMs makes it difficult to investigate how a GenAI tool arrived at a specific answer.

How Knowledge Graphs Provide a Solution

Knowledge graphs offer a structured approach to data storage and retrieval, overcoming the limitations of traditional LLMs. By implementing knowledge graphs, organizations can:

• Ensure accuracy and reliability: Knowledge graphs store complex, structured data and the relationships between them, ensuring the accuracy of Gen AI’s output, bolstering its reliability and effectiveness.
• Improve transparency and explainability: Knowledge graphs allow users to inspect how LLMs arrive at answers, validating information sources and building trust in GenAI outputs. Results become explainable, repeatable, and transparent, enhancing trust across various sectors like life sciences, manufacturing, and telecommunications.
• Enhance data accessibility: Knowledge graphs act as a bridge, translating user intent into actionable queries that LLMs can execute with greater accuracy. This empowers users, regardless of their technical knowledge, to interact with GenAI more effectively.
• Enable dynamic query templates: Knowledge Graphs enable Large Language Models to dynamically employ multiple query templates to match specific business scenarios, making interactions with Gen AI more nuanced.
• Improve relevance of insights: Knowledge graphs ensure insights generated by LLMs are deeply informed by underlying data structures, helping enterprises efficiently transform raw data into actionable intelligence.
• Democratize data: By making it easier for everyone in an organization to interact with generative AI, Knowledge Graphs can free up experts to focus on tasks that require their specific skills.

Key Benefits

• Increased Trust: Knowledge graphs boost confidence in GenAI outputs, making it easier for organizations to embrace these systems.
• Nuanced Interactions: Large Language Models can dynamically employ multiple query templates to match specific business scenarios, making interactions with Gen AI more nuanced.
• Democratized Data Access: Knowledge graphs make it easier for everyone in an organization to interact with generative AI, freeing up experts to focus on specialized tasks.

Conclusion

As generative AI evolves, it’s critical to provide users with the means to fact-check its outputs. Knowledge graphs offer a powerful solution, enhancing trust, improving accuracy, and democratizing data access within organizations.

Further Reading

Explore Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 8, for more insights from leading experts and partners like Dassault Systèmes and Neo4j.