Large language models (LLMs) like Claude, and GPT-4o have impressive capabilities but face two major limitations: the knowledge they contain is frozen in time (more specifically, at training time), and the context windows that determine how much information they can process at once are finite. Two approaches that can address these limitations are Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP). In this article, I'll provide an overview of how they both work, along with some differences that distinguish them from each other.

Retrieval-Augmented Generation

RAG is a technique that enhances LLMs by incorporating a separate retrieval system that collects relevant information from external sources before the model generates a response. RAG works using three main steps,

  1. Query Processing: The user's query is processed to identify key information needs.
  2. Retrieval: Relevant documents or information snippets are fetched from external databases or knowledge bases.
  3. Augmented Generation: The retrieved documents are added to the context window of the LLM, which then generates a response based on both its pre-trained knowledge and the collected information.

This approach bridges the gap between static pre-trained knowledge and dynamic information retrieval systems.

RAG Model Diagram

Key Benefits of RAG

  • Enhanced accuracy that provides factual, up-to-date information
  • Reduced hallucinations by using information in the knowledge base
  • Customizable knowledge obtained from domain-specific sources
  • Transparency through citations provided by the source

Imagine a university chatbot that is prompted by a student:

"When is the CS301 final exam?"

Using a RAG implementation, the system would:

a) Process this query
b) Retrieve the current semester's exam schedule from a university database
c) Provide this information to the LLM along with the query

The LLM would then generate an accurate response with up-to-date information,

"The CS301 final exam is scheduled for December 15th at 2:00 PM in Lecture Hall B."

RAG allows systems to access up-to-date information and specialized knowledge without retraining the model.

Model Context Protocol

MCP uses a different approach to extending AI capabilities. While RAG focuses on retrieval before generation, MCP provides a standardized interface for LLMs to request additional information or perform actions during the generation process. MCP works by,

  1. Recognition: The model recognizes when it needs additional information or tools.
  2. Protocol Execution: Following a predefined protocol, the model outputs a structured request.
  3. External Processing: This request is handled by external systems to fetch data or perform actions.
  4. Continued Generation: The model incorporates the results and continues its response.

MCP Model Diagram

Key Benefits of MCP

  • Context optimization to make the most of limited context windows
  • Structured information using schemas and formats that models understand better
  • Information hierarchy that prioritizes crucial information for the task
  • Consistency which provides standardized formatting for predictable model behavior
  • Performance improvement that achieves better reasoning with the same context size

MCP is especially valuable when dealing with complex tasks that require multiple information sources but must operate within the constraints of a model's context window capacity.

Using the university chatbot scenario implemented with MCP, when a student asks about the CS301 exam:

a) The model recognizes it needs the current exam schedule
b) It produces a structured MCP call:

{action: "fetch_exam_schedule", course: "CS301", semester: "current"}

c) An external system processes this call and returns the exam details

The model incorporates this information into the response,

"The CS301 final exam is on December 15th at 2:00 PM in Lecture Hall B."

Conclusion

Both RAG and MCP are powerful approaches to extending AI capabilities beyond their initial training limitations. RAG is generally easier to implement, and works well for straightforward information retrieval. MCP offers more flexibility for complex, multi-step tasks requiring various tools, and data sources.

In practice, many advanced AI systems are beginning to combine elements of both approaches - using RAG for broad knowledge access and MCP for specific tool use and dynamic information retrieval. As you start to develop more AI applications, consider whether one approach, or a combination of both suits your specific use case.