Several new protocols enhance the capabilities of large language models. In this context I am taking (1) Retrieval-Augmented Generation and (2) Model Context Protocol. They stand out as powerful approaches, addressing distinct needs.
Understanding the first AI architecture here: RAG
It is short for Retrieval-Augmented Generation and accentuates and enables large language models with some knowledge from outside. When a database expert makes a query, RAG retrieves relevant information from documents, databases, web pages and then uses this information to amplify and augment the LLM’s prompt, resulting in accurate, factual, and up-to-date response.
How does it work?
RAG stores external vectors for semantic search. It identifies relevant content pieces based on the user’s query. It receives the retrieved context along with the original query and generates an informed response.
Where is RAG used?
It is used to answer questions based on internal documentation, policies, or product specifications in Enterprise Search. It provides consistent answers to customer inquiries in customer Support. It generates articles or summaries grounded in specific source materials pertinent to content creation.
The second AI architecture, called Model Context Protocol or MCP
Different from RAG, that we just discussed, MCP interacts with APIs, and live data sources, making real-time information accessible to large language models. This architecture enables the LLM to interface with CRM, ERP, and weather APIs, making the outcome more relevant and contextual.
Where is an MCP used?
MCP can be used to create tickets, update records, or schedule appointments through integrated systems. It accesses (1) live inventory levels, (2) financial data, or (3) sensor readings.
Differentiating AI Architectures in Action MCP vs RAG
You might want to use MCP ….keep reading
If your primary focus is to ground the LLM in specific, verifiable information and reduce factual errors. When you have a large volume of existing documents or data you want the LLM to leverage.
Alternatively, RAG should be used if…
You want the large language model to access live, dynamic data from APIs or databases and to interact with external systems in real time.
In which direction to go when the two AI architectures look just similar?
The decision between RAG and MCP is not always an either/or. In many advanced AI applications, a hybrid approach combining both architectures delivers the most comprehensive solution. RAG can provide the LLM with the necessary knowledge to understand a request, while MCP can then enable the LLM to execute actions or retrieve real-time data based on that understanding.
For instance, a customer service AI might use RAG to answer common questions from a knowledge base and then employ MCP to create a support ticket or update a customer’s profile in a CRM system.
Conclusive
Retrieval-Augmented Generation and Model Context Protocol should be used together to create more powerful AI systems. RAG is used for retrieving information from a knowledge base, while MCP is used for taking actions or interacting with external systems. A common integration pattern is to use RAG for the initial retrieval of information, followed by using MCP to take a specific action based on that retrieved context.
Frequently Asked Questions
Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) are methods for giving AI models relevant, up-to-date information. RAG is for retrieving information from documents, while MCP enables AI to interact with live systems and perform actions.
Retrieval-Augmented Generation FAQs
| Question | Answer |
| What is RAG? | RAG lets an AI model look up information in external documents (like your company’s policy manual or a knowledge base) before answering a question. Using this gives relevant answers based on your specific data, not just what it learned during training. |
| Why is RAG useful? | It allows the AI to use the most current information, even if it was published after the AI was initially built. |
| What kind of data does RAG use? | RAG works best with unstructured data like text documents, PDFs, articles, and internal manuals. |
| When should I use RAG? | RAG is great for situations where you need to answer questions based on a large library of existing knowledge, such as a customer support bot answering policy questions or an internal tool for looking up technical documentation. |
Model Context Protocol FAQs
| Question | Answer |
| What is MCP? | MCP allows models to connect with and control external tools and applications, such as databases, APIs, or user interfaces. |
| Why is MCP useful? | It lets AI agents perform real-world actions and access live, dynamic data. |
| What kind of systems does MCP use? | MCP primarily interacts with structured data and live services via APIs. |
| When should I use MCP? | MCP is ideal when you have questions like “What’s the status of my order?” or “Schedule a meeting for tomorrow”. |
RAG and MCP Together
| Question | Answer |
| Are RAG and MCP competitors? | No, they are complementary and solve different problems. |
| Can I use RAG and MCP together? | Yes. Many advanced AI systems combine them: RAG handles the general knowledge retrieval from documents, while MCP accesses live systems for specific, up-to-date facts or to perform tasks. |
| Which one should I choose? | If your main goal is to improve the accuracy of answers from a knowledge base, start with RAG. If you need your AI to interact with live, changing data in applications, MCP should be used. |
