Are you interested in educating your LLM with knowledge graphs? Download this brochure to learn more.
Are you interested in the intersection of large language models (LLMs) and knowledge graphs? This brochure provides information about the considerations for solving your company’s LLM Implementation challenges.
- Ensure accuracy and reduce hallucinations: LLMs can fabricate information not based on reality. Rely on a factual foundation for reliable sources of truth to anchor and validate responses using knowledge graphs, reducing the risk of errors.
- Enable reasoning and contextual understanding: Knowledge graphs allow LLM output to be supported by reason. With structured domain representation, GenAl is enhanced by providing context, which furthers understanding.
- Facilitate knowledge retrieval and integration: LLMs are limited to public knowledge that lacks external source input. Knowledge graphs enrich and integrate valuable unstructured and structured data, delivering relevant information for LLM responses.
- Provide explainability and transparency: Don’t suffer from answers lacking background or quality data. Knowledge graphs enable provenance and provide credibility, accuracy, and traceability of output, delivering greater LLM response validity.
