RAG

Large Language Models demonstrate impressive capabilities in natural language understanding and generation. However, they operate as closed systems trained on static datasets, lacking real-time awareness of new information and struggling with factual accuracy. Two prominent approaches have emerged for RAG: Vector Databases and Knowledge Graphs.

Generative AI is transforming how businesses generate content, but LLMs still grapple with serious challenges in enterprise settings—especially around domain-specific context and the risk of hallucinations. This is where Graphshare leverages Retrieval-Augmented Generation to enhance precision and trustworthiness.