For operational use, LLMs must be fed governed enterprise data in real time. Combining a trusted semantic data layer with the Model Context Protocol (MCP) is the right combo.
In the era of GenAI apps powered by LLMs, the combination of an AI data layer and MCP AI is emerging as a critical AI design pattern.
While powerful LLMs – like Gemini, GPT-4o, or Claude – can reason, remember, and converse, they’re basically limited to generic public information, and frozen in time (on the date their training ended)
The only way to ground them with information about your specific business (your customers and business transactions, for example) is to safely integrate live enterprise data into the prompting process.
This article discusses the value of an AI data layer coupled with MCP, and why it is the ideal solution for enabling contextual, grounded, and real-time operational workloads, such as generative AI use cases in customer service.
Prompt engineering for large language models (LLMs) is a sophisticated art and science that moves beyond simple queries to create powerful, tailored AI applications. While it may seem like a complex task, this approach to crafting prompts is essential for unlocking the full potential of LLMs. By providing clear, well-structured instructions, users can guide the model to produce personalized and highly accurate real-time responses, making it possible to create dynamic, context-aware user experiences that were previously out of reach.
Ultimately, the true value of prompt engineering lies in its ability to ground the model and prevent the common problem of "hallucinations", where the AI fabricates information. This technique allows for system grounding, ensuring the LLM's outputs are based on specific, provided data rather than its general, pre-trained knowledge. A key benefit is that it enables domain-specific behavior without the need for expensive and time-consuming model retraining. This efficiency allows developers and businesses to quickly adapt off-the-shelf LLMs to specialized tasks, from legal document analysis to medical diagnosis assistance, simply by engineering the right prompt.
Before you can prompt at scale, however, you first need to address 4 key challenges:
Knowing what context is needed
Deciding which data sources to get the context from
Retrieving and harmonizing the data
Formulating the context efficiently, safely, and cleanly
…and all this at the speed of conversational AI.
Fortunately, two constructs have evolved that enable us to overcome these prompt engineering challenges.
The first construct enables prompt engineering at scale is the Model Context Protocol (MCP), a standard means for a AI client applications (MCP clients) to connect with external applications, tools, and resources through an MCP server. MCP enables us to systematically:
Understand user intent
Identify what context the model needs
Retrieve the needed enterprise data
Assemble context based on policy constraints
Inject context into the response sent to the GenAI client
In most enterprises, the data needed to fulfill a user query is scattered across dozens of systems with no unified API, schema, or key resolution strategy.
Instead of the MCP server trying to federate across those systems, it makes a single call to the AI data layer, like:
GET /data-products/customer?id=ABC123
AI data layer based on business entities
When the AI data layer is based on business entities (such as customers, loans, orders, or devices) :
Every field in the data product is mapped to the appropriate underlying system.
Transformation logic is applied (status normalization, date formats).
All the relevant data for each instance (a single customer, for example) is secured, managed, and stored in its own Micro-Database™.
Real-time access is supported via API, and updates can be continuous or on-demand.
End-to-end workflow
Here’s how the full stack works:
[User query] > [MCP server] > [AI data layer] > [MCP prompt] > [LLM] > [Response]
MCP server limitations
A common misconception is that the MCP server uses an LLM to construct prompts. It doesn’t – and shouldn’t . AI prompt engineering is handled by deterministic logic, not generative models.