Generative semantic fabric // GSF
Knowledge Graphs (KGs) are useful but do not provide complete knowledge representation // loss of details and context.
The article critiques the popular Retrieval Augmented Generation (RAG) method for enhancing Large Language Models (LLMs) by integrating custom data. While RAG boosts LLMs without retraining and reduces AI hallucinations, it has several drawbacks. Implementing RAG is labor-intensive, requiring manual data preparation and ongoing maintenance. Ensuring response quality and accuracy demands robust evaluation tools, and RAG struggles with complex, domain-specific queries. Additionally, RAG presents challenges in data consistency, incurs high costs, and poses security risks.
To address these issues, the article introduces Generative Semantic Fabric, an automated knowledge graph of semantic embeddings that simplifies data relationships and business logic for BI and LLM tools. This approach ensures a consistent, accurate data view, minimizes maintenance, reduces costs, and enhances security by automating sensitive data handling. It integrates seamlessly with existing data infrastructures, supports scalability, and effectively manages domain-specific knowledge. The Generative Semantic Fabric is positioned as a more robust, reliable, and sustainable alternative to RAG, suitable for most organizations.
