Let me explain it clearly in one breath: LLM, MCP, EMB

In many articles introducing AI, some terms frequently appear, such as LLM, MCP, and EMB. It is a bit difficult for ordinary people to understand what these professional terms mean, what are their functions, and what are the technical principles behind them.

In this article, I will try my best to use easy-to-understand language to explain what these terms mean and what their functions are, to help everyone better understand AI.

1. LLM: Large Language Model

LLM stands for Large Language Model. It refers to an artificial intelligence basic model based on deep learning technology. Through training with a large amount of text data, it gradually acquires the ability to understand and generate natural language.

There are three basic definitions of LLM: based on deep learning, processing natural language, and large parameter scale . Among them:

• Based on deep learning : A neural network based on the Transformer technology architecture captures long-distance dependencies in the input sequence through the self - attention mechanism to achieve a global understanding of the text.
• Processing natural language : performing tasks such as question answering, text generation, and translation/dialogue is a form of generative AI.
• Large parameter scale : In order to understand natural language well, it is necessary to split natural language into the smallest granularity parameters (Tokens). However, different permutations and combinations of natural language contain multiple meanings. Therefore, a large model usually contains tens of billions or even hundreds of billions of parameters.

LLM is an important part of the field of natural language processing (NLP). Most of the large models launched by major manufacturers that we are familiar with are LLM.

The training process of LLM is to capture language patterns, research structures and semantic associations from massive text data through the self-attention mechanism. Its core goal is to predict the next word or sequence through probabilistic modeling, and ultimately generate coherent and contextual text.

This is also why the big model is said to be a probabilistic prediction machine and why information illusion occurs.

Of course, because of the different types of training data, the big models also have different capabilities. For example, for data in finance, law, psychology, medical and other types, the big models trained have higher accuracy in the corresponding fields. The multimodality mentioned in many articles can be simply understood as the model base that can process multiple different types of information .

Going further, the so-called adaptive capability of Baidu Wenku's current AI model can analyze user input and automatically match the corresponding domain model .

The training process of a large model can be simply divided into two parts: pre-training and post-training.

• Pre-training (imitation learning) imparts basic capabilities, that is, from zero to good capabilities, such as the imitation learning stage of AlphaGo.
• Post-training (reinforcement learning) optimizes behavioral capabilities. It is a process from good to excellent, and good behaviors are continuously solidified through reinforcement learning.

In the field of large models, typical representative models can be roughly divided into three types:

• Llama( Meta ) : Open source model that supports code generation and scientific reasoning.
• BERT ( Google ) : A bidirectional Transformer-based model that excels at text classification and semantic understanding (such as search engine optimization).
• GPT series ( OpenAI ) : realize multi-round dialogue and complex task processing through generative pre-training, such as GPT-4 supports multimodal input.

Of course, the large model still has limitations, mainly in the following aspects:

• Information hallucinations : It is possible to generate text that appears plausible but is factually incorrect.
• Data bias : Bias in the training data can lead to discriminatory output.
• Computational cost : Training a 100-billion-scale model requires thousands of GPU hours, which is very expensive.

2. MCP: Model Context Protocol

The full name of MCP is ( Model Context Protocol). In layman's terms, MCP is an abstract technical architecture design concept .

MCP is an open standard protocol designed to solve the communication problem between large language models (LLMs) and external data sources and tools. It enables AI systems to easily interact with various external resources by providing a unified interface and protocol. It was developed and open sourced by Anthropic.

Basic definition of MCP: A standardized communication protocol for establishing seamless connections between AI models and external data sources.

It defines common interfaces that allow AI assistants to dynamically access and integrate external data sources (databases, APIs, file systems) and tools and services (computing tools, search engines), thereby expanding the functionality of AI and improving its efficiency, similar to the USB and Type-C interfaces in the AI ​​field .

MCP is based on a client-server architecture and consists of the following key components:

• MCP Host : An application that runs LLM (such as the Claude client) and is responsible for initiating task requests.
• MCP Client : Maintains a 1:1 connection with the server within the Host, parses task requirements and coordinates resource calls.
• MCP Server : As the middle layer, it dynamically exposes available tools, APIs, and data sources (such as local files, external databases) to AI Agents and provides security control and context management.
• Local/Remote Services : Includes local resources (file system) and remote services (such as GitHub, Google Maps API).

In the workflow, MCP Server defines capabilities in layers (such as data reading, function calling, and prompt templates) to enable AI Agent to automatically match tools according to task requirements and perform operations through Function Calling , such as querying a database or calling an API, ultimately generating a multi-step coherent response.

Compared with traditional API interfaces, MCP has the following differences:

To sum up, the technical advantages of MCP are mainly as follows:

• Simplify integration : Reduce the complexity of integrating AI with external tools through a unified interface to avoid fragmentation issues.

• Security and controllability : MCP supports two-way connections, ensures data security, and provides fine-grained control.

• Flexibility and scalability : MCP supports autonomous workflow decision-making and orchestration and is applicable to a variety of cross-platform scenarios.

We can regard MCP as a universal socket in the field of AI technology. We can integrate various AI tools and data sources through MCP without doing special adaptation work .

3. EMB: Data Embedding Technology

The full name of EMB is ( Embedding ). Simply put, it is a technology that converts low-quality data into high-quality benchmark data that can be used for reference.

Professionally explained, EMB is a technology that can map high-dimensional, sparse discrete data ( words in text, image features, or category labels ) to low-dimensional, dense continuous vector space.

EMB is widely used in natural language processing (NLP), recommendation systems, image processing, computer vision and other fields. Its core idea is to capture the potential semantic relationship and similarity between objects by mapping objects (such as words, sentences, users, items, etc.) into low-dimensional vectors, thereby achieving dimensionality reduction, feature extraction and modeling goals.

These vectors can capture the semantic information and internal relationships of the original data, making it easier for machine learning models to process and analyze them. The following figure shows the role of EMB: Typical application scenarios of Embedding in natural language processing (NLP) include:

• Text classification converts text into word vectors (such as Word2Vec or GloVe) to classify documents, such as spam filtering and topic classification.
• Machine translation : Capturing word semantics and contextual relationships to improve the effectiveness of neural network machine translation (NMT) and make the translation results closer to "human language".
• Named Entity Recognition (NER)  : Through embedding technology, the model can better understand the entities and their relationships in the text, thereby improving the accuracy of named entity recognition.
• Sentiment analysis : Use word embedding to capture the emotional tendency of words and help the model determine the emotional polarity of the text.
• Recommender system : Generate vector representations of users and items to improve the accuracy of the recommendation system by capturing potential relationships.
• Semantic search : By mapping queries and documents into the same vector space, efficient semantic similarity calculation is achieved.
• Text similarity calculation : Use word embedding to capture the semantic similarity between words for tasks such as sentence matching and text similarity analysis.
• Transfer learning and feature extraction : High-quality word embeddings can be transferred to downstream tasks without relearning the meaning of vocabulary, thereby expanding the scope of application.

The technical characteristics of EMB can be summarized into three main points:

• Semantic preservation : Through training, the positions in the vector space reflect the intrinsic relationship of the data (such as the inclusion relationship between "apple" and "fruit").
• Generality and transferability : The same embedding model can serve a variety of downstream tasks (such as classification, clustering, and recommendation).
• Dimensionality reduction and feature extraction : compress high-dimensional sparse data (such as one-hot encoded text) into low-dimensional dense vectors to reduce computational complexity.

Embedding technology has been widely used in various fields of AI and is one of the core technologies of LLM.