As enterprises increasingly adopt AI solutions, including the use of Large Language Models (LLMs) ensuring responsible AI governance has become a priority. One approach is to leverage an Enterprise Semantic Model (ESM) to provide a structured framework. An ESM defines organization’s data, processes, and policies and enables AI systems to make informed decisions that align with enterprise standards and policies.

What is a Large Language Model?

A Large Language Model (LLM) is an advanced form of artificial intelligence trained on vast amounts of text data (Bommasani, et al, 2022). Using deep learning architectures, primarily the Transformer model, LLMs learn statistical patterns of language. This allows them to generate text, answer questions, summarize content, translate languages, and more with remarkable fluency.
However, while powerful, LLMs are not databases. They don’t “know” everything. Instead, they predict what is most likely to come next in a sequence of words, based on training data. Without context, they may generate incorrect information or information misaligned with the organization’s data, processes, and policies.

How Do Large Language Models Work?

At the core of an LLM are billions (or even trillions) of parameters that capture language patterns, grammar, concepts, and relationships. When given a prompt, the model:

1. Tokenizes text into smaller units.
2. Embeds these tokens into a high-dimensional space.
3. Predicts the next token by analyzing context and probability.
4. Generates coherent output iteratively.

This architecture enables LLMs to handle diverse tasks. But the challenge is ensuring their responses are accurate, contextual, and grounded in reliable information, especially for enterprise use cases.

Using Retrieval-Augmented Generation (RAG) to Provide Context

Retrieval-Augmented Generation (RAG) (Martineau, 2023) is an AI framework for retrieving context related data including organization policies from a knowledge base to improve LLM responses. It enhances LLMs by coupling them with a search or retrieval system. Instead of relying solely on the pretrained LLM, RAG injects relevant, up-to-date, and domain-specific information into the prompt.

Here’s how it works:

1. User Query – A request is made to the system.
2. Retrieve – Searches a knowledge base (documents, metadata, databases) for relevant content.
3. Augmentation – The retrieved context is fed into the LLM prompt.
4. Generation – The LLM produces a response, grounded in the provided context.

This approach significantly reduces incorrect or “hallucinated” information and ensures that outputs remain accurate and aligned with the organization’s data, processes, and policies.

What is an Enterprise Semantic Model?

An Enterprise Semantic Model (ESM) provides an enterprise-level understanding of an organization’s data, processes, and policies. It defines common business meanings to organization data with relationships to other entities. By representing this knowledge, ideally as a graph, ESMs enable AI systems to make informed decisions that align with enterprise standards and policies.  

Why does a Semantic Model Matter?

While RAG improves accuracy, the quality of context matters just as much. A semantic model, an organized representation of concepts, relationships, and meaning, ensures that information retrieved and fed into the LLM is consistent, precise, and business-relevant. It ensures AI systems operate within established policies and guidelines, reducing compliance risks.

For example, in the utility industry, terms like “meter read,” “transformer asset,” or “outage management” carry very specific meanings. Without a semantic model, an LLM may conflate these terms or misinterpret their relationships. With a semantic model, the system understands how these entities connect, making retrievals more accurate and responses more trustworthy.

Semantic models, part of metadata, effectively provide the language of the enterprise to guide both retrieval and reasoning to guide AI responses, ensuring they align with enterprise policies.

Knowledge Graph: Fueling LLM + RAG with Metadata

Enterprise semantic modeling and metadata management takes this a step further by providing a knowledge graph (of metadata. Unlike raw text search, semantic model management enables the organization of enterprise metadata, data about data, into a connected knowledge graph (Erlinger & Wöß, 2016) structure. This enables:

• Precise Retrievals – The graph links terms, definitions, lineage, and relationships across systems.
• Context-Rich Prompts – RAG can use a metadata graph to supply structured, accurate, and consistent context.
• AI-Ready Data –Harmonizing metadata through semantic models ensures LLMs operate with trusted inputs for tuning internal parameters.
• Cross-System Intelligence – Utilities and enterprises can query across silos, bridging data stored in Customer Care & Billing Systems (CC&B), Meter Data Management Systems (MDMS), Outage Management Systems (OMS), Distributed Energy Resource Management Systems (DERMS), and more.

In short, semantic modeling and metadata management transforms disconnected metadata into a connected semantic layer that supercharges both LLMs and RAG.

Conclusion

Large Language Models are transformative, but their true power is unleashed when combined with retrieval and semantics. RAG ensures responses are grounded in real data, while semantic models guarantee consistency and clarity. A knowledge graph of metadata ties it all together, providing enterprises with the confidence that their AI solutions are not only intelligent but also accurate, contextual, and aligned with their business reality. An ESM offers a solution for enforcing AI governance in an enterprise context by providing a pre-defined data model for both prompt and response layers with established policies and guidelines.  

---

References

Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A.,  Creel, K.,  Davis, J.Q., Demszky, D., …Liang, P. (2021). On the Opportunities and Risks of Foundation Models.. ArXiv. https://crfm.stanford.edu/report.html

Erlinger, L. & Wöß, W. (2016). Towards a Definition of Knowledge Graphs. Retrieved October 8, 2025, from https://ceur-ws.org/Vol-1695/paper4.pdf

Martineau, K. (2023, August 22). What is retrieval-augmented generation?. IBM. What is retrieval-augmented generation (RAG)? – IBM Research

SemanticWorx. (2025). Affirma. Enterprise Semantic Model. https://www.semanticworx.com/capabilities/enterprise-semantic-model/

SemanticWorx. (2025). Affirma. Semantic and Ontology Management. https://www.semanticworx.com/solution-semantic-and-ontology-management/

---

About the Authors

Michael Covarrubias is a dynamic, results-driven senior leader with a proven record of delivering top-level IT leadership and building cost-efficient organizations aligned with business strategy. He brings deep expertise in enterprise technology that underpins his leadership, vision, strategic development, and execution capabilities. Most recently, he was responsible for establishing a clients data architecture model and program. He developed the strategy and architecture for their Data Lake initiative, overseeing activities such as RFP development, technology fit-gap analysis, solution selection, and implementation. In addition, he led the design and execution of the enterprise data governance program, ensuring robust frameworks and practices to support data quality, compliance, and long-term scalability.

Dr. Shawn Hu is a system integration specialist with more than 20 years of industry experience in CIM-based enterprise integration, interface design, and implementation. He provides consulting services to utility customers in North and South America. His business expertise includes data warehouse, BI analytics, SOA implementation, Web services design (WSDL), standards-based data architecture (IEC CIM/ MultiSpeak), and XSD/JSON implementation. Most recently he has been leading development of enterprise data models, including Grid Connectivity, Weather, DERs, and advanced analytics models, based on the CIM. He delivered implementation artifacts in XSD, JSON Schema, and Oracle SQL DDL to support scalable, standards-based solutions. With a deep interest in AI, LLM and ML, he continues to innovate for the benefit of his clients.

---