6 Key Steps to Using Large Language Models with Your Internal Data

The rapid evolution of artificial intelligence — and particularly large language models (LLMs) — has unlocked unprecedented opportunities for businesses to leverage their internal data in new ways. 

According to Gartner, by 2026, more than 80% of enterprises will have used generative AI APIs or models, or deployed generative AI-enabled applications in production environments, up from less than 5% in early 2023. This surge in adoption is driven by the transformative potential of combining LLMs with an organization’s proprietary data, enabling more personalized, contextually aware, and accurate AI-powered insights.

But realizing these benefits isn’t automatic. The path to success with LLMs requires careful attention to data quality, metadata management, retrieval strategies, and governance. What should data leaders know to seize the opportunity of enterprise data for LLM use cases? Here’s what you should know.

#1: Define clear business objectives

Before deploying LLMs on internal data, organizations must set clear business goals. Whether the aim is to improve customer service, enhance operational efficiency, or support better decision-making, these objectives shape how data is selected, processed, and used to train and augment LLMs. Without this clarity, projects risk becoming unfocused or failing to deliver measurable business value.

#2: Use Retrieval-Augmented Generation (RAG) to combine internal and external data

One of the most effective ways to enhance LLM performance is through retrieval-augmented generation (RAG). RAG enables models to dynamically fetch and incorporate up-to-date information from trusted internal sources — from product specs and shipping records to customer feedback and sales reports — enriching responses with real-time relevance and accuracy.

#3: Recognize the value of unstructured and tribal knowledge

LLMs thrive when they have access to all forms of organizational knowledge — not just structured data from databases, but also unstructured content (like documents, emails, and presentations) and tribal knowledge (the undocumented, experience-based insights held by employees). Unlocking this full spectrum of information is critical to building truly useful and contextually aware AI solutions.

What does this mean in practice? Include a wide range of collaborators on your AI project, and be sure to connect with tenured experts within the business as you scout out potential inputs for your model. Seek out people who can give you the context on why and how data was collected to ensure you leverage this data appropriately.

#4: Prioritize metadata management to ensure trust and traceability

Metadata — data about data — plays a vital role in making internal data usable for LLMs. It provides essential context about data origin, quality, relevance, and lineage. Effective metadata management allows organizations to track the source and reliability of data feeding into LLMs, ultimately increasing trust in the model’s outputs. A data catalog serves as a critical enabler here, acting as the metadata hub that helps users and systems understand what data exists, where it came from, and how to interpret it.

#5: Consider knowledge graphs and vector databases for richer context

To better represent relationships between concepts within unstructured and semi-structured data, organizations should explore knowledge graphs and vector databases. Knowledge graphs capture the connections between data points, enabling more nuanced and semantically aware retrieval. Vector databases, meanwhile, excel at similarity searches across complex data types like images, audio, or free text — making them especially useful for enriching LLM responses.

#6: Establish strong data governance and observability

AI success depends on data you can trust. Organizations must ensure data governance frameworks are in place to manage access, quality, and compliance, particularly when sensitive internal data is involved. 

Data observability also becomes essential, providing visibility into data reliability, freshness, and performance over time. Together, governance and observability help maintain the integrity of data feeding into LLMs.

Conclusion: AI success starts with trusted internal data

The potential for large language models to transform internal data into valuable business insights is enormous — but success depends on getting the data foundation right. Organizations must clearly define their business goals, implement RAG to blend internal and external knowledge, manage metadata effectively, and explore advanced tools like knowledge graphs and vector databases. Strong data governance and observability further ensure the reliability and relevance of the data powering AI.

By following these best practices, data leaders can maximize the value of LLMs, driving innovation, efficiency, and better decision-making across the enterprise.

Curious to learn how a data catalog can support your next AI success? Book a demo today.