AI Technical Glossary: A Complete Guide to 270+ Terms
Series - AI LLM Systematic Tutorials
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This article was last updated on 2025-07-30, the content may be out of date.
AI Technical Glossary
This reference gathers the core terminology of the LLM field, from basic concepts to advanced technical architecture, to help you build a systematic understanding of the AI technology landscape.
📚 Fundamentals
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| AGI (Artificial General Intelligence) | An AI system with human-level intelligence | An all-capable AI that can think, learn, and create like a person | A robot that can write poetry, code, cook, and chat at the same time |
| LLM (Large Language Model) | A large neural network model trained on massive data | A “super brain” that understands and generates human language | GPT-4, Claude, ERNIE Bot, and the like are all LLMs |
| Training | The process of fitting neural network parameters on large data | The AI’s “study phase” — like a person absorbing knowledge from books | Training a model on all text on the internet until it learns language |
| Inference | A trained model generating output from input | The AI’s “application phase” — like a person answering questions from what they learned | The model generating an answer after you ask it a question |
| Token | The smallest unit of text a model processes, a fragment split by a tokenization algorithm | The particles of AI language, processed one at a time | "我喜欢苹果" → ["我", "喜欢", "苹果"] |
🏗️ Architecture
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Transformer | A deep learning architecture based on self-attention, proposed by Google in 2017 | The “neural skeleton” of modern AI that lets models understand language efficiently | GPT, BERT, and every other large model is built on Transformer |
| Encoder | A neural network component that encodes an input sequence into semantic representations | The AI’s “understanding unit” — turns text into vectors machines understand | BERT uses an encoder for text understanding tasks |
| Decoder | A neural network component that generates output token by token based on context | The AI’s “writing unit” — generates answers from what it understood | The GPT series are all decoder-only models |
| Self-Attention | A mechanism that computes how much each element in a sequence relates to the others | The AI automatically “focuses on what matters”, like a person picking out key points while reading | In “deposit money at the bank”, “bank” attends to “money”; in “fish by the river bank”, it attends to “river” |
| Multi-Head Attention | Several self-attention mechanisms run in parallel to capture different types of dependencies | The AI understands text from multiple angles at once | One head tracks syntax while another tracks semantics |
| Positional Encoding | Vector representations that add position information to each token | Lets the model know “who comes first, who comes later” | “The dog bit the man” and “the man bit the dog” mean different things |
| Query | The vector that actively asks for related information — what the current word needs | The numeric expression of “what am I looking for” | “Apple” queries attributes like taste and color |
| Key | The identifier vector for information being queried — what each word can offer | The label of “what I can provide” | “Sweet” serves as the Key for a taste feature, waiting to be queried |
| Value | The representation vector holding the actual content and true semantic information | “My actual content”, in numbers | The actual semantic representation of “sweet”: [0.8, 0.2, -0.1] |
| Attention Weight | Importance scores expressing how much to attend, usually normalized via softmax | “How much to pay attention”, quantified | 0.8 means strong attention, 0.1 weak; all weights sum to 1 |
| Cross-Attention | Attention across two sequences — Query comes from one, Key/Value from another | Cross-modal information exchange | In image-text matching, text Queries attend to image Keys/Values |
| Causal Attention | Attention restricted to the current position and earlier, preventing future information leaks | Attention that can “only look backward” | When GPT generates the 5th word it can only see the previous 4 |
| Softmax Function | An activation function that turns any real-valued vector into a probability distribution | Converts scores into “importance percentages” | [2,1,0] → [0.67,0.24,0.09], preserving relative magnitudes |
🔢 Mathematical Representation
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Vector | A mathematical object with magnitude and direction; an ordered list of numbers | A “numeric ID card” that describes a thing with numbers | [25, 180, 70] can represent a person’s age, height, and weight |
| Embedding | The technique of mapping discrete symbols into a continuous vector space | Turns words into “numeric coordinates” | "king"→[0.25, -0.12, 0.78, ...] |
| Query / Key / Value | The three core vector matrices in self-attention: what is asked, what is labeled, what is delivered | Query = what I want, Key = what I can offer, Value = my actual content | Query=[0.1,0.2] asks about taste, Key=[0.8,0.1] labels sweetness, Value=[0.9,0.05] is the actual representation of sweetness |
| Feed-Forward Network | Applies an independent nonlinear transform at each position | Deepens the model’s understanding of each word | From “spring” the model further associates “warmth, growth” |
| Layer Normalization | Standardizes a layer’s inputs | A “stabilizer” for training | Prevents gradient explosion or divergence |
| Residual Connection | A cross-layer connection that preserves the original information | An “express lane” for information, preventing loss | Like a shortcut path that keeps deep networks from degrading |
🔄 Processing Pipeline
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Tokenizer | Converts text into a sequence of tokens | A “knife for chopping text” | "Hello world" → ["Hello", " world"] |
| Context Window | The maximum number of tokens a model can process | The AI’s “memory limit” | GPT-4 has a 128K context |
| Decoding | Generates text token by token from a probability distribution | The AI’s “writing process” | Starts generating from the most probable word |
| Temperature | A parameter controlling generation randomness | A “creativity dial” | High temperature is more creative, low more stable |
| Top-p Sampling | A sampling strategy based on cumulative probability | An “essence filter” | Only considers candidates whose cumulative probability reaches 90% |
| Max Tokens | Caps the length of generated output | A “word-count limiter” | Keeps the AI from answering too long |
🛠️ Engineering Practice
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| RAG (Retrieval-Augmented Generation) | An AI approach combining retrieval and generation | An “open-book exam” AI | Look up references first, then answer the question |
| Prompt Engineering | The craft of designing and optimizing prompts | “The art of asking” | Helping the AI understand your needs better |
| Fine-tuning | Training a pretrained model on a specific task | “Targeted job training” | Turning a general model into a medical assistant |
| BPE (Byte Pair Encoding) | A common tokenization algorithm | A “text compression technique” | "unhappiness" → ["un","happi","ness"] |
| Detokenization | Turns a token sequence back into readable text | “Reassembling the pieces” | ["我","喜欢","苹果"]→"我喜欢苹果" |
| Streaming | Generates output token by token in real time | The “typewriter effect” | A chatbot thinking while it types |
🧠 Classic Models Compared
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| RNN (Recurrent Neural Network) | A neural network that processes sequences step by step | A “read-one-word-at-a-time AI” | Translating "我爱你" word by word |
| LSTM (Long Short-Term Memory) | An improved RNN that handles long-range dependencies | “A better memory” | Can remember content from the beginning |
| CNN (Convolutional Neural Network) | A neural network that excels at image patterns | An “image specialist” | Recognizing cats, dogs, and faces |
| Encoder-Decoder Architecture | A model containing both understanding and generation modules | An “all-round AI” | Machine translation models |
📊 Application Scenarios
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Chat product | A user-facing AI application interface | An “AI chat shell” | ChatGPT, Claude |
| API call | An interface for program-to-program communication | The “AI phone line” | An application calling the OpenAI API |
| Context management | The technique of maintaining conversation history | The “AI’s memory” | A chatbot remembers what you said |
| Multi-turn dialogue | A continuous human-machine interaction mode | “Ongoing conversation” | Ask about the weather, then what to wear |
| Function Calling | The model invoking external APIs to perform tasks | The “AI’s ability to act” | The AI checks the weather or searches automatically |
🧩 Model Optimization and Training Techniques
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| LoRA (Low-Rank Adaptation) | Fine-tunes model parameters via low-rank matrices | “Lightweight fine-tuning” | Lets an LLM quickly adapt to a new domain |
| Quantization | Represents model parameters at lower precision | “Slimming the model down” | FP32→INT8 speeds up inference |
| Pruning | Removes redundant neurons or connections | “Trimming the branches” | Cutting useless parameters |
| Distillation (Knowledge Distillation) | A large model teaches a small one | “Teacher trains the student” | GPT-4 teaching a small model |
| Checkpoint | A saved intermediate state during model training | A “training save point” | Prevents losing progress on a power cut |
🔍 Vector Retrieval and Knowledge Integration
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Embedding Model | A model that converts text into semantic vectors | A “semantic coordinate machine” | text-embedding-3-large |
| Vector Database | A database supporting vector retrieval | A “semantic warehouse” | Milvus, Pinecone, FAISS |
| Cosine Similarity | Measures how similar two vectors’ directions are | A “semantic similarity meter” | A cat is sleeping ≈ The cat is resting |
| Knowledge Graph | Stores knowledge as nodes and relationships | A “knowledge map” | apple → is a → fruit |
| Hybrid Search | Combines semantic retrieval with keyword matching | “Belt-and-suspenders search” | Searching both cat and pet animal at once |
🧩 Multimodal and Agents
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Multimodal Model | Handles text, images, audio, and other modalities at once | A “full-senses AI” | GPT-4V, Gemini |
| VLM (Vision-Language Model) | Vision-Language Model | An AI that can “see pictures” | A visual question-answering AI |
| Speech Recognition | Converts speech to text | A “dictation AI” | Voice input methods |
| TTS (Text-to-Speech) | Converts text to speech | An “AI announcer” | The AI reads its answer aloud |
| AI Agent | An AI capable of autonomous action and decision-making | An AI assistant “that can act” | Devin, AutoGPT |
⚙️ Model Evaluation and Safety
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Hallucination | The model generating false information | “Confident nonsense” | Inventing papers or facts |
| Alignment | Bringing the model in line with human values | “Values training” | Tuning a model with RLHF |
| RLHF (Reinforcement Learning from Human Feedback) | Optimizes a model with human preferences | “Humans teaching AI to speak” | How ChatGPT was trained |
| Red Teaming | Adversarial testing of model safety | A “security penetration test” | Testing whether the model leaks secrets |
| Bias | Systematic prejudice in model outputs | “The AI plays favorites” | Preference for a gender or language |
🧰 Emerging Trends and Future Directions
| Term | Technical definition | Plain-language explanation | Example |
|---|---|---|---|
| Mixture of Experts | A structure with multiple sub-models activated dynamically | A “panel-of-experts AI” | The Gemini 1.5 Pro architecture |
| Context Compression | Compresses conversation history to save tokens | “Memory compression” | Summarizing long conversations |
| Memory-Augmented Model | An AI combined with long-term memory mechanisms | An AI “with a memory” | ChatGPT long-term memory |
| Autonomous Agent | An AI that can plan and execute tasks on its own | A “self-managing AI” | AutoGPT, Devin |
| Synthetic Data | Virtual training data generated by AI | “AI-made textbooks” | Expanding a training set with AI |
💡 Study Advice
🎯 Priority for Mastering Core Concepts
- Beginner (must know): Token, Embedding, Transformer, LLM
- Intermediate (important): Self-Attention, RAG, Context Window
- Advanced (optional): LoRA, Mixture of Experts, Red Teaming
📖 Suggested Learning Path
- Understand the basics: what a token is, and why vector representation is needed
- Master the core architecture: the Transformer encoder-decoder structure
- Practice application techniques: combining prompt engineering with RAG
- Go deeper into technical details: attention mechanisms and alignment training
🔗 Concept Map
Fundamentals → Math → Architecture → Processing → Engineering → Optimization → Retrieval → Agents
↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
Token → Vector → Transformer → Decoding → RAG → LoRA → Embedding → Agent
LLM → Q/K/V → Attention → Context → Prompt → Quant → Knowledge → Memory🚀 Tip: the AI technology landscape is vast but tightly interconnected. A good approach is to study it systematically along four dimensions: understand → build → optimize → secure.
📚 Further Reading
🔗 AI LLM Systematic Tutorials Series
- A Complete Guide to AI LLMs — tokens and vectors explained in depth, from the ground up
- Transformer Architecture Deep Dive — the attention mechanism and the core technology behind LLMs
- Prompt Engineering Complete Guide — a hands-on guide from prompt engineering to context engineering
- [This post] AI Technical Glossary — 270+ terms and a dictionary of the AI technology landscape
🎯 How to Use This Page
- Order of study: work through tutorial 1 → tutorial 2 → tutorial 3 systematically, and keep this page handy as a reference dictionary
- Looking up terms: when you hit an unfamiliar term while reading the other tutorials, just search for it here
- Building the knowledge system: combine the tutorials’ theory with the term explanations here to build a complete picture of AI
📘 Appendix: English–Chinese AI Glossary (A–Z)
| English term | 中文名称 | Brief explanation |
|---|---|---|
| AGI (Artificial General Intelligence) | 通用人工智能 | AI with human-level general intelligence |
| Alignment | 对齐 | Making AI behavior conform to human values |
| API (Application Programming Interface) | 应用程序接口 | The standard way programs call each other |
| AutoGPT / Autonomous Agent | 自主智能体 | AI systems that plan and execute tasks on their own |
| BERT (Bidirectional Encoder Representations from Transformers) | 双向Transformer编码模型 | A landmark pretrained NLP model |
| Bias | 偏差 | Systematic unfairness in model output |
| BPE (Byte Pair Encoding) | 字节对编码 | A widely used text tokenization algorithm |
| Checkpoint | 检查点 | A saved intermediate state during model training |
| CNN (Convolutional Neural Network) | 卷积神经网络 | A network architecture that excels at image recognition |
| Context Window | 上下文窗口 | The maximum number of tokens a model can process |
| Context Compression | 上下文压缩 | Summarizing history to save context |
| Cosine Similarity | 余弦相似度 | A metric for semantic similarity between vectors |
| Decoder | 解码器 | The network module that turns semantic vectors into text |
| Decoding | 解码过程 | The process of a model generating text |
| Detokenization | 反分词 | Turning a token sequence back into text |
| Distillation (Knowledge Distillation) | 知识蒸馏 | A large model guiding a small one’s learning |
| Embedding | 嵌入 | Mapping discrete words into a continuous vector space |
| Embedding Model | 向量模型 | A model that produces semantic vectors for text |
| Encoder | 编码器 | A network component that turns text into semantic representations |
| Encoder–Decoder | 编码–解码结构 | A model architecture with both understanding and generation |
| Feed Forward Network (FFN) | 前馈网络 | The nonlinear transform module inside a Transformer layer |
| Fine-tuning | 微调 | Further training a pretrained model on a specific task |
| Function Calling | 工具调用 | A model’s ability to call external APIs to take actions |
| Hallucination | 幻觉 | The model generating false or fabricated information |
| Hybrid Search | 混合检索 | Combining semantic retrieval with keyword search |
| Knowledge Graph | 知识图谱 | A network storing knowledge as nodes and relationships |
| Layer Normalization | 层归一化 | Standardizing a network layer’s inputs |
| Latency | 延迟 | Response time from model input to output |
| LLM (Large Language Model) | 大语言模型 | A language model trained on massive corpora |
| LoRA (Low-Rank Adaptation) | 低秩适配 | A lightweight model fine-tuning method |
| LSTM (Long Short-Term Memory) | 长短期记忆网络 | An RNN variant that captures long-range dependencies |
| Memory-Augmented Model | 记忆增强模型 | AI with long-term memory capability |
| Mixture of Experts (MoE) | 专家混合模型 | An architecture that dynamically selects sub-models to cooperate |
| Multi-Head Attention | 多头注意力 | A mechanism computing several attentions in parallel |
| Positional Encoding | 位置编码 | A way to add position information to tokens |
| Pruning | 剪枝 | Removing redundant parameters to shrink a model |
| Prompt Engineering | 提示工程 | Optimizing prompts to improve model output quality |
| Quantization | 量化 | Representing model parameters at lower precision for performance |
| Query / Key / Value (QKV) | 查询 / 键 / 值 | The three elements of self-attention |
| RAG (Retrieval-Augmented Generation) | 检索增强生成 | Combining external knowledge retrieval with generation |
| Red Teaming | 红队测试 | Evaluating model safety through adversarial inputs |
| Residual Connection | 残差连接 | A cross-layer information pass-through that prevents gradient degradation |
| RLHF (Reinforcement Learning from Human Feedback) | 人类反馈强化学习 | Optimizing model output with human preferences |
| RNN (Recurrent Neural Network) | 循环神经网络 | A network that processes sequences step by step |
| Self-Attention | 自注意力 | A mechanism computing relationships between sequence elements |
| Streaming | 流式输出 | A model generating and emitting output as it goes |
| Synthetic Data | 合成数据 | Virtual training data generated by AI |
| Temperature | 温度参数 | The parameter controlling generation randomness |
| Throughput | 吞吐量 | The number of requests processed per second |
| Token | 词元 | The smallest unit of text a model processes |
| Tokenizer | 分词器 | The tool that splits text into tokens |
| Top-p Sampling | 累积概率采样 | A generation strategy that filters low-probability words |
| Transformer | Transformer架构 | The core neural network built on attention |
| TTS (Text-to-Speech) | 文本转语音 | Turning text into natural speech |
| Vector | 向量 | A mathematical structure representing an entity’s features numerically |
| Vector Database | 向量数据库 | A system that stores vectors and retrieves them semantically |
| VLM (Vision-Language Model) | 视觉语言模型 | A model that understands both images and language |
| Weight | 权重参数 | The core learnable numeric parameters of a model |
