RAG, Agent, MCP: A solution for large models

Abstract: In today's digital age, big models have become the focus of artificial intelligence due to their powerful language understanding and generation capabilities. Whether it is intelligent customer service, content creation, or data analysis, big models have shown great potential and provide us with

The big models have brought many conveniences to our lives and work. However, the seemingly omnipotent big models are actually facing many difficult dilemmas. The main problems are illusions , lack of autonomy, and difficulty in calling tools. Faced with these dilemmas, are big models helpless? Of course not! The emergence of technologies such as RAG (retrieval-augmented generation), Agent, and MCP (model context protocol) is like a beacon that lights up the way forward for big models, bringing hope for solving these problems.

RAG: Make the big model “well-founded”

1. What is RAG?

RAG, or Retrieval-Augmented Generation, is a technology that combines information retrieval with text generation. Simply put, it is like equipping a large model with an intelligent search engine. When a user asks a question, RAG will first search an external knowledge base, database, or document library to find information related to the question, and then use this information as a reference and input it into the language model, allowing the model to generate answers based on this information. This approach breaks the limitation of large models relying only on internal pre-trained knowledge, making the generated content more accurate, rich, and consistent with actual conditions.

2. Solving the illusion problem of large models

The "hallucination" problem of large models mentioned above can be effectively alleviated with the help of RAG. Since large models are trained based on a large amount of text data, when generating content, fictitious information that does not match the facts may appear. RAG introduces an external knowledge base to provide the model with real and reliable information support when generating answers.

Take the medical field as an example. When a patient asks about the treatment of a certain disease, if the big model relies solely on its own knowledge, it may give wrong or inaccurate advice due to the limitations of the training data or misunderstandings. However, if RAG technology is used, the model will first retrieve relevant treatment plans, clinical cases and other information from the authoritative medical literature database, and then generate answers based on the retrieved content, thereby greatly reducing the risk of giving wrong treatment advice. In the financial field, when analyzing market trends or investment strategies, RAG can retrieve the latest market data, industry reports, etc., to provide real-time and accurate information for the big model, avoiding the model from generating "hallucinations" based on outdated or inaccurate data and giving misleading investment advice.

3. Breaking through knowledge limitations

The training corpus of large models has certain limitations in time and space. On the one hand, the training data is often collected within a certain period of time and cannot cover the latest knowledge and information; on the other hand, the source and scope of the data are also limited, making it difficult to include knowledge in all fields and scenarios. This leads to the possibility of knowledge gaps or inaccurate answers when large models face some time-sensitive or highly professional questions.

The emergence of RAG has made it possible for large models to break through this limitation. By retrieving external knowledge sources in real time, RAG can dynamically update the knowledge reserves of large models, allowing the model to obtain the latest information and professional knowledge. In news reports, events often occur and develop in real time. Large models can use RAG technology to retrieve the latest news information in a timely manner and conduct comprehensive and accurate reporting and analysis of events. In the field of scientific research, new research results are constantly emerging. RAG can help large models quickly obtain the latest scientific research literature, understand cutting-edge research trends, and provide valuable references for researchers.

4. Ensuring data security and explainability

In terms of data security, for some private data of enterprises or institutions, RAG can store this data in a local knowledge base. When the big model generates answers, it retrieves relevant information from the local knowledge base without uploading private data to the cloud for training, thereby effectively protecting the security and privacy of the data. In the legal industry, the company's contracts, legal documents, etc. contain a lot of sensitive information. Using RAG technology, companies can build legal knowledge graphs locally. When dealing with legal issues, the big model retrieves information from the local knowledge graph to ensure that data is not leaked.

Explainability has always been a difficult problem for large models. Since the parameters and calculation processes within the model are very complex, it is difficult for users to understand the basis for the model to generate answers. RAG makes the answers of large models explainable by establishing a connection between the retrieved information and the generated content. Users can clearly see which retrieved information the model is based on to generate answers, so that they can have a more intuitive judgment of the reliability and accuracy of the answers. In academic research, when a large model answers an academic question, RAG can display the retrieved relevant academic literature, allowing researchers to trace the source of the answer and evaluate the credibility of the answer.

Agent: Give large models the ability to think autonomously

1. Agent concept analysis

The large model agent is an autonomous intelligent body based on the large-scale language model (LLM). It can make complex decisions and execute tasks by understanding and generating natural language, and has a certain degree of autonomy and interaction. Unlike traditional AI systems, the agent can not only answer questions, but also actively complete a series of complex tasks. If the large language model is compared to a "super brain", then the AI ​​Agent is to equip this brain with "hands and feet" and "tools", so that it can take the initiative to act like a human, rather than just passively answer questions. It can understand natural language instructions, make autonomous decisions based on environmental changes, and call various tools to complete tasks, realizing the transformation from "passive response" to "active action".

2. Improving autonomy and flexibility

Traditional AI systems usually run based on rules or pre-set algorithms, lacking autonomy and flexibility. They can only perform tasks according to fixed procedures and are difficult to cope with complex and changing environments. The emergence of Agents provides a new way to solve this problem. Agents can simulate human perception, decision-making and action processes, make decisions autonomously according to changes in the environment, and take corresponding actions. In the intelligent customer service scenario, traditional customer service systems can often only answer common questions based on preset words, and it is difficult to provide effective solutions for some complex problems or special needs of users. Agent-based intelligent customer service can understand user problems, autonomously search knowledge bases, call related tools, and even interact with other systems, thereby providing users with more personalized and accurate services. In the field of data analysis, Agents can autonomously select appropriate analysis methods and tools based on the analysis objectives and data characteristics, and automatically generate analysis reports, greatly improving the efficiency and quality of data analysis.

3. Enhance the ability to handle complex tasks

When faced with complex tasks, Agent can break them down into multiple subtasks and complete them collaboratively by calling external tools and services. Taking travel planning as an example, users only need to tell Agent the destination, time, budget and other information of the trip, and Agent can automatically plan the travel route, book air tickets, hotels, arrange tickets for attractions, and even recommend special food and niche attractions based on local weather and user interests. In this process, Agent will call search engines to obtain relevant information about the destination, call online travel platforms for reservations, and call map services to plan routes, just like an experienced travel butler, providing users with one-stop service. Agent can also combine RAG technology to retrieve relevant information from a large amount of text, provide richer knowledge support for the execution of tasks, and further improve the processing capabilities of complex tasks.

MCP: Building a bridge between large models and the outside world

1. What is MCP?

MCP, or Model Context Protocol, is an open protocol designed to achieve seamless integration between large language model (LLM) applications and external data sources, tools, and services, similar to the HTTP protocol in the network or the SMTP protocol in email. It improves the functionality, flexibility, and scalability of LLM applications by standardizing the way models interact with external resources, and provides developers with a unified, efficient, and interoperable development environment. Simply put, MCP is like a "universal socket" in the AI ​​world, allowing large models to easily connect to various external tools and data sources to achieve more powerful functions.

2. Solving the tool calling problem

Before the emergence of MCP, large models faced many difficulties in calling external tools. Different tools often have their own unique API designs and usage specifications, just like each electrical appliance has a plug of a different shape. For large models to use these tools, they need to write a lot of specific code for each tool to adapt its interface. Every time developers integrate a new tool, they need to rewrite the adaptation logic, which greatly increases development costs and time. Due to the lack of unified protocols or specifications, each AI application has to implement tool integration logic separately, which not only leads to duplication of workload, but also makes it difficult for systems to collaborate or reuse existing results.

The emergence of MCP provides an effective solution to these problems. It defines a unified interface and protocol, just like equipping all electrical appliances with a unified standard plug, enabling AI to communicate directly with any tool server that complies with the MCP standard, eliminating the cumbersome custom integration process. This not only reduces the difficulty of development, but also improves development efficiency, allowing large models to more easily call various external tools to achieve functional expansion. With MCP, large models no longer need a complex adaptation process when calling search engines to obtain information and calling code development tools to implement functions. They can complete the call through a unified interface, greatly improving the efficiency and flexibility of tool calls.

3. Promoting multi-scenario applications

MCP has a wide range of application scenarios and has important applications in many fields such as office, academic, and life services, providing strong support for large models to play a role in different scenarios.

In smart office scenarios, MCP Server can connect to various internal systems, such as mail servers, calendars, and document management systems, enabling AI assistants to manage meetings, automatically record meeting content, generate meeting minutes, and create to-do items based on discussions; they can also process emails, classify important emails, draft replies, and set reminders; and in document collaboration, they can find information in team documents, provide editing suggestions, and track changes. A manager can ask the AI ​​assistant: "Collate the key points of all sales meetings last week and create a list of action items." The AI ​​assistant can automatically complete this task by accessing the meeting record system and project management tools through MCP Server.

In the field of academic research, MCP can also play an important role. When conducting literature reviews, researchers often need to consult a large number of academic documents. MCP can help large models connect to academic databases, quickly retrieve relevant documents, and analyze and summarize the contents of the documents, providing valuable references for researchers. Large models can also use MCP to call data analysis tools to process and analyze experimental data, helping researchers draw conclusions.

In terms of life services, MCP enables the big model to provide better convenience for users. Users can use voice commands to let the smart assistant equipped with MCP call map navigation, flight query, hotel reservation services and other tools to directly arrange travel to "grab your bag and go". When the user says "I want to travel to Beijing next week, help me plan the itinerary", the smart assistant can connect to the relevant travel service platform through MCP, query scenic spot information, book air tickets and hotels, and plan travel routes, providing users with one-stop travel services.

The three work together to build a more powerful large-scale model ecosystem

RAG, Agent, and MCP are not isolated technologies. They collaborate and complement each other to build a more powerful large-model ecosystem. RAG provides Agent with a rich knowledge reserve, so that Agent has a more solid factual basis for decision-making and action, and can make more intelligent decisions. When Agent is dealing with a complex problem, it can retrieve relevant knowledge and information through RAG to avoid blind decision-making. MCP provides convenience for Agent to call various external tools, allowing Agent to make full use of external resources and further expand its capabilities. In data analysis tasks, Agent can call professional data analysis tools through MCP, combined with industry data and analysis methods retrieved by RAG, to quickly and accurately complete data analysis tasks.

With the continuous development and improvement of technology, the application prospects of RAG, Agent, and MCP will be broader. In the future, we are expected to see them being deeply applied in more fields, such as intelligent diagnosis in the medical field, risk prediction in the financial field, personalized learning in the education field, etc., to provide stronger support for solving various complex problems. They will also continue to promote the development of large model technology, so that it can better serve mankind and bring more convenience and innovation to our lives and society.