Get ready to unlock the full potential of your AI models with enhanced context, security, governance, data quality, and business SLOs through the power of the data product layer.
The Surge in AI Adoption
Enterprise AI has moved from experimentation to production. Modern AI models—including large language models (LLMs)—now power everything from customer-facing chatbots and knowledge search to coding assistants and real-time analytics. Organizations are embedding these models into critical workflows to automate decisions, accelerate execution, and uncover new revenue opportunities.
As adoption matures, the challenge has shifted. Deploying a model is no longer enough; reliability, accuracy, and trustworthiness determine whether AI initiatives succeed at scale.
Models need trusted, contextual, and well-governed data to generate outputs that enterprises can rely on. Without that foundation, even the most advanced models risk producing errors, hallucinations, or results that can’t be operationalized.
How LLMs are Helping Businesses Scale
LLMs are versatile tools capable of handling a wide range of tasks due to their training on vast datasets and ability to understand and generate human-like text. Here are some areas where LLMs excel:
- Natural Language Processing: Engaging in conversations, text completion, and content creation like articles and stories.
- Language Translation and Summarization: Translating text between languages and condensing long texts into shorter summaries.
- Question Answering and Sentiment Analysis: Providing accurate answers to questions and analyzing emotional tones in text.
- Content Personalization and Creative Writing: Generating personalized recommendations and creating imaginative stories and poems.
- Educational and Programming Assistance: Explaining complex topics, helping with homework, generating code snippets, and debugging.
- Data Analysis: Interpreting data and generating insights for decision-making.
These capabilities make LLMs valuable across various industries, including technology, education, healthcare, entertainment, and more.
According to VentureBeat analysis, despite all the use cases that AI and ML can offer industries, 87% of AI projects never go into production. That's shocking!
Ensuring the accuracy and reliability of LLMs remains the most significant challenge due to their complexity and the vast amount of unstructured data they process with a lack of domain-specific context and biased responses. Let's understand this in detail.
Navigating the Challenges of Enterprise AI Models
Core Limitations of LLMs in Enterprise Use
Numerous public and internal Large Language Models (LLMs) have been criticized for their inconsistent performance. Enterprises building their LLMs encounter substantial challenges, such as a lack of domain-specific context and biased responses. Despite these issues, organizations continue to invest in LLMs to revolutionize customer interactions, optimize operations, and enable innovative business models for enhanced growth. Let's first understand how LLMs function and why these issues occur so frequently. LLMs are trained on vast amounts of unstructured data; they learn about the data and store this information as part of weights and biases in their neural network; this information includes the language understanding and the general knowledge provided in the training data.
Fundamental 1: LLMs work on the knowledge they have been trained on, and a question asked outside that knowledge may receive an inaccurate response. When we use these LLMs for Enterprise use cases, we must know that they may not understand your domain-specific questions completely and will most likely hallucinate. So, we need to make them aware of the domain knowledge to answer our specific questions, and that's why RAG (Retrieval Augmented Generation) is becoming the most popular way of providing domain knowledge to LLMs. But vanilla RAG can only go so far.
Fundamental 2: LLM primarily operates on the principle of "Garbage in, Garbage out." The higher the quality of data it is trained on, the better the result it will give. Unfortunately, off-the-shelf LLMs are not prepared with structured and relevant data for enterprise use cases. However, by providing high-quality structured data as a part of the context, LLM can do a great job.
Enterprises face substantial challenges when deploying LLMs and other AI models in production. While these models can generate human-like responses and perform complex tasks, they are often inconsistent, prone to hallucinations, and limited in domain-specific understanding. This stems from how they are built: trained on massive unstructured datasets, they encode general knowledge in their weights but lack the semantic context needed for reliable enterprise use.
Two core principles explain these challenges:
- Limited contextual awareness – Models are only aware of what they are trained on. Without specific domain context, their responses to enterprise-specific queries will often be incomplete or inaccurate.
- Garbage in, garbage out – LLMs operate on the “Garbage in, Garbage out” principle. The higher the quality of training data, the better the result will be.
Data Access Scenarios in the Enterprise
There are two scenarios for enterprise LLM data access:
Unorganized data pools – Giving LLMs direct access to unorganized data like data lakes, legacy systems, or loosely structured files rarely produces reliable results. Without clear definitions of business entities, metrics, and relationships, models generate inefficient or incorrect queries, struggle with contextual understanding, and risk hallucinations that drive up compute costs and erode trust.
Organized catalogs – A more effective approach starts with structured data catalogs and semantic layers or knowledge graphs. These define metrics, hierarchies, and relationships in a business-friendly way, giving LLMs the context they need to generate accurate queries and reduce hallucinations.
This approach enables trustworthy outputs, improves self-service analytics, and lays the foundation for autonomous AI agents that can perform multi-step analysis and decision support. Even with this method, enterprises must continuously maintain and govern their catalogs and semantic layers to keep AI outputs reliable at scale.
The Role of RAG
Retrieval-Augmented Generation (RAG) enhances LLM performance by supplying models with relevant, real-time context from enterprise data sources. Its effectiveness, however, depends heavily on the quality and semantic clarity of the underlying data.
Modest improvements on unorganized data – RAG can pull snippets from unstructured documents or loosely organized sources, but without clear data structures or semantics, the LLM still struggles to generate precise, actionable insights or accurate queries against structured systems. Hallucinations and inefficiencies in data interpretation persist.
Highly effective with semantic layers – Enterprise-grade RAG shines when paired with structured catalogs and semantic layers or knowledge graphs. In this scenario, RAG can retrieve business definitions, relationships, query patterns, and relevant sections of knowledge graphs, providing the model with the building blocks and instructions it needs to produce reliable, production-grade outputs. This is where the combination of model reasoning + domain-aware context delivers real enterprise value.
Understanding The Root Cause of the LLM Problems
Large Language Models (LLMs) face several challenges that can lead to perceived failures in their performance. Here are some of the main factors contributing to the limitations of AI projects in production:
Lack of Contextual Understanding
- LLMs do not inherently understand the semantics of user queries or specific business domains.
- Without deeper comprehension, they may generate incorrect or irrelevant outputs, especially for structured queries like SQL.
- Example: A “customer name” field might be labeled cust, customer_name, or C-Name across different systems. Without added context, the model can’t reliably connect them.
- This disconnect between raw data and meaning leads to misinterpretations and unreliable insights.
Poor Data Quality
- The accuracy of LLMs is heavily dependent on the quality of their training or contextual data.
- If the data is outdated, inconsistent, or noisy, the model may produce queries that don’t align with actual database schemas.
- This results in errors, broken outputs, or missing information, even if the user's intent is valid.
Privacy Issues
- LLMs trained on or exposed to sensitive data can inadvertently reveal confidential information.
- For example, a prompt like “retrieve customer details with recent transactions” might cause the model to generate unsafe queries accessing protected fields.
- Without proper safeguards, this creates serious data privacy and compliance risks.
Technical Debt and Infrastructure Constraints
- Integrating LLMs with legacy systems is often complex and resource-intensive.
- Common barriers include outdated tech stacks, insufficient compute, and lack of real-time processing capabilities.
- This creates bottlenecks in deployment, scaling, and operational efficiency for LLM-powered applications.
These factors collectively contribute to the difficulties encountered by LLMs, resulting in perceived failures or limitations in their performance and reliability. But how do we bring all these pieces together effectively?
The Solution: Building LLMs Powered by Data Products
Enter the data product era! A data product is a comprehensive solution that integrates Data, Metadata (including semantics), Code (transformations, workflows, policies, and SLAs), and Infrastructure (storage and compute). It is specifically designed to address various data and analytics (D&A) and AI scenarios, such as data sharing, LLM training, data monetization, analytics, and application integration.
Across various industries, organizations are increasingly turning to sophisticated data products to transform raw data into actionable insights, enhancing decision-making and operational efficiency. Data products are not merely a solution; they are transformative tools that prepare and present data effectively for AI consumption—trusted by its users, up-to-date, and governed appropriately.
DataOS, the world's first comprehensive data product platform, enables the creation of decentralized data products that deliver highly contextual data, enhancing the performance of LLMs such as Llama, Databricks, or OpenAI. DataOS takes your data strategy to the next level by catering to a slice of data required to power your LLMs.
The Data Product Layer sits between your raw data layer and consumption layer and performs all those critical tasks needed to improve the accuracy and reliability of your LLMs. These core tasks include - enriching metadata to make data more contextual, creating semantic data models without moving any data, and applying governance policies, data quality rules, and SLOs. On top of this, it also provides an infrastructure layer to autonomously run these data products without requiring any dependency on your DevOps or development team.
Now let's understand in detail how a DataOS's Data Product Layer can work as a solution to all your LLM problems that we just discussed:
Providing Rich Contextual Information
DataOS provides over 300 pre-built connectors to integrate with your raw data layer that covers various data sources, including data lakes, databases, streaming applications, SaaS applications, and CSVs. Once all relevant data sources are connected, all the metadata gets scanned from these systems. Metadata enrichment becomes crucial, allowing users to add descriptions, tags, and glossary terms for each attribute, making unstructured source data meaningful.
This ensures that duplicate data stored across different systems with pseudo names are accurately understood and used with proper context.
DataOS Glossary serves as a common reference point for all stakeholders, ensuring everyone understands key terms and metrics consistently. By providing descriptions, synonyms, related terms, references, and tags for each business term, the Glossary helps maintain data consistency.
While AI models can generate synonyms, their accuracy is often low. Synonyms defined under the Glossary are extremely helpful for models to match different tables or columns stored across various sources accurately. Tags enrich metadata by adding descriptive information, enhancing searchability, context, and data discovery, thereby supporting data governance and user collaboration.
Elevating Data Quality
We can run data profiling workflows to check the incoming data's completeness, uniqueness, and correctness beforehand so that we can easily avoid using bad data for analytics or training LLMs.
We can build a logical model once we have all the enriched metadata. This logical model focuses on entities, attributes, and their relationships without considering physical storage details, ensuring data integrity and reducing redundancy.
DataOS simplifies the creation of logical models specific to a data product by defining relationships between entities and including measures and dimensions for each. This process requires no physical data movement, as it deals solely with enriched metadata.
By perfectly mapping the logical model to physical data, DataOS ensures accurate and complete data, significantly enhancing data quality. Transforming unstructured data into a structured format enables faster query responses and facilitates exploratory data analysis.
When a user queries the LLM model, it utilizes the semantic model to access all required data instantly, as the data is now well-structured with proper semantics and context. This results in clean and brief queries, avoiding complex JOINs and sub-optimized queries.
Enhancing Security and Governance
The DataOS data product platform addresses privacy issues by incorporating robust data governance and security policies that prevent the accidental exposure of sensitive information. By employing robust access and data policies (masking and filtering), DataOS ensures that access to sensitive data is tightly controlled and monitored. Only authorized personnel can access sensitive PII information with these policies in place. This prevents the LLM from generating SQL queries that expose confidential data without appropriate safeguards.
Switching to Open, Scalable, and Composable Data Infrastructure
DataOS mitigates technical debt and infrastructure challenges as it seamlessly integrates with your existing infrastructure, including legacy systems, without necessitating a rip-and-replace of your current data stack.
DataOS is designed to bridge the gap between traditional databases and modern AI technologies, ensuring compatibility and enhancing computational efficiency.
Leveraging advanced metadata management and optimized data processing capabilities, DataOS enables real-time query generation without overburdening legacy infrastructure. This ensures that organizations can deploy LLM-based solutions smoothly, minimizing operational inefficiencies and unlocking the full potential of AI.
Furthermore, every data product comes with storage and compute provisioned during development. Hence, the data consumers can autonomously consume data products without depending on your DevOps or development team.
Additionally, DataOS offers a Data Product Hub, a marketplace for data products to make it easy for data consumers to autonomously access and consume data required to power their use case. Each data product available on the Data Product Hub contains schema, semantics, SLAs (response time, freshness, accuracy, and completeness), quality checks, and access control information that helps you power any AI model with accurate, reliable, and trustable data.
Optimizing LLM Performance: The Benefits of Using a Data Product Layer
Using a data product layer on top of your existing data infrastructure significantly enhances the performance and accuracy of large language models (LLMs) in several ways:
- Enhanced Context and Understanding: The data product layer provides LLMs with a comprehensive framework of data relationships and hierarchies, enabling more accurate interpretations and reducing errors.
- Efficient Query Generation: By organizing data with enriched metadata, the data product layer allows LLMs to generate highly accurate and effective queries, mitigating the risks of poorly structured inputs.
- Improved Data Quality and Governance: Data products ensure high data quality and robust governance, providing LLMs with accurate, consistent, and well-governed data, thus enhancing model performance and reliability.
- Scalability and Flexibility: Creating reusable and comprehensive data products with modular architecture provides complete scalability and adaptability to evolving business needs.
- Cost Efficiency: The comprehensive data products and semantic data modeling reduce computational overhead, lowering costs for data processing and query execution.
- Enhanced User Experience: Accurate, contextually relevant, and easily interpretable data leads to more meaningful and actionable insights from LLMs, improving overall user experience.
In the era of AI-driven decision-making, the ability to deliver high-quality, context-rich data to LLMs is not just an advantage—it's a necessity. As we look to the future, thriving companies will effectively harness these advanced data management capabilities, turning their vast data resources into actionable insights and competitive advantages.
In conclusion, integrating a Data Product Layer with your existing data infrastructure represents a significant advancement in leveraging Large Language Models (LLMs) for enterprise data management.
This powerful combination enhances LLMs' contextual understanding and query precision and ensures robust data governance, scalability, and operational efficiency. In navigating the complexities of big data and AI, organizations benefit from solutions like DataOS, which help in building and managing data products in a decentralized way.
Curious to learn how DataOS can elevate your data strategy and drive better business outcomes? Contact us today!
