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Benefits of Wren Engine with LLMs

The integration of a Modeling Definition Language (MDL) with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems represents a significant leap forward in enhancing the capabilities of AI in understanding and generating human-like text. By leveraging MDL to structure semantics, relationships, calculations, and aggregations in a comprehensible and organized manner, the data becomes more accessible and interpretable for LLMs. This structured approach allows for the storage of semantics in a vector store, which LLMs can then utilize to perform semantic searches across contexts with greater accuracy and relevance.

Enhancing LLM and RAG with MDL: A Synergistic Approach​

  1. Structured Semantic Understanding: With semantics stored in a structured way, LLMs can access a rich, organized repository of context, meaning, and relationships. This structured understanding aids LLMs in grasping the nuances of language and context more effectively, leading to more accurate and contextually relevant outputs.
  2. Semantic Search Across Contexts: The ability to store semantics in a vector store enables LLMs to perform semantic searches, retrieving information that is not just keyword-based but related to the semantic context, such as calculations, aggregations, and relationships. This capability is crucial for RAG implementations, where the retrieval component must fetch the most relevant context to augment the generation process.
  3. Enhanced Understanding of Relationships, Calculations, and Aggregations: By encoding relationships, calculations, and aggregations within the MDL, LLMs can achieve a deeper understanding of the data's underlying structure and logic. This understanding allows LLMs to generate responses that are not only contextually accurate but also logically consistent with the data model.
  4. Improved Data Governance and Security: Incorporating ABAC and RBAC within the MDL ensures that LLMs operate within the bounds of data governance policies and security protocols. This feature is particularly important when LLMs access sensitive or regulated data, as it ensures compliance and protects privacy.