Original | Odaily Planet Daily (@OdailyChina)
Author | Golem (@web 3_golem)
Now, if people in the crypto circle don't pay attention to AI, they are easily ridiculed (yes, my friend, think about why you clicked in).
Are you completely clueless about the basic concepts of AI, asking Doubao what every abbreviation in a sentence means? Or are you confused by various technical terms at AI offline events, pretending you haven't lost track?
Although it's unrealistic to jump into the AI industry overnight, knowing the high-frequency basic vocabulary of the AI industry is always beneficial. Fortunately, the following article is prepared for you↓ Sincerely recommend that you read it thoroughly and bookmark it.
Basic Vocabulary (12)
LLM (Large Language Model)
LLM is essentially a deep learning model trained on massive amounts of data, excelling at understanding and generating language. It processes text and is increasingly capable of handling other types of content.
In contrast, there is SLM (Small Language Model)—typically emphasizing lower cost, lighter deployment, and more convenient localization of language models.
AI Agent (AI Intelligent Agent)
AI Agent refers not just to "a model that can chat," but to a system that can understand goals, call tools, execute tasks step by step, and even plan and verify when necessary. Google defines an agent as software that can reason based on multimodal inputs and perform actions on behalf of the user.
Multimodal (Multimodal)
Such AI models don't just read text; they can simultaneously process and generate various types of input and output, including text, images, audio, and video. Google explicitly defines multimodality as the ability to process and generate different types of content.
Prompt (Prompt)
The instruction input by the user to the model, which is the most basic form of human-computer interaction.
Generative AI (Generative AI / AIGC)
Emphasizes AI's ability to "generate" rather than merely classify or predict. Generative models can generate text, code, images, memes, videos, and other content based on prompts.
Token (Token)
This is one of the concepts in the AI circle most similar to a "Gas unit." Models don't understand content by "word count" but process input and output by tokens. Billing, context length, and response speed are often strongly related to tokens.
Context Window (Context Window / Context Length)
Refers to the total number of tokens a model can "see" and utilize at once. It can also be described as the number of tokens the model can consider or "remember" during a single processing instance.
Memory (Memory)
Allows the model or Agent to retain user preferences, task context, and historical states.
Training (Training)
The process by which the model learns parameters from data.
Inference (Inference Execution)
Opposite of training, it refers to the process where the model receives input and generates output after deployment. The industry often says, "Training is expensive, but inference is even more costly," because many costs during the actual commercialization phase occur during inference. The distinction between training and inference is also a fundamental framework in discussions about deployment costs among major vendors.
Tool Use / Tool Calling (Tool Calling)
Means the model doesn't just output text but can also call tools such as search, code execution, databases, and external APIs. This is already considered one of the key capabilities of an Agent.
API (Interface)
The infrastructure used when AI products, applications, or Agents connect to third-party services.
Advanced Vocabulary (18)
Transformer (Transformer Architecture)
A model architecture that enables AI to better understand contextual relationships. It is the technical foundation for most large language models today. Its biggest feature is the ability to simultaneously consider the relationships between every word in an entire segment of content.
Attention (Attention Mechanism)
It is the most critical core mechanism of the Transformer. Its role is to allow the model to automatically determine "which words are most worth focusing on" when reading a sentence.
Agentic / Agentic Workflow (Agentic / Agentic Workflow)
This is a very hot term recently. It means a system is no longer just "question and answer" but has a certain degree of autonomy to break down tasks, decide next steps, and call external capabilities. Many vendors see it as a sign of "moving from Chatbot to an executable system."
Subagents (Sub-agents)
An Agent further breaks down into multiple specialized smaller Agents to handle sub-tasks.
Skills (Reusable Capability Modules)
With the popularity of OpenClaw, this term has become noticeably more common recently. These are installable, reusable, and composable capability units/operation manuals for AI Agents. However, there are specific warnings about tool misuse and data exposure risks.
Hallucination (Machine Hallucination)
Refers to the model speaking nonsense with a straight face, "perceiving patterns that do not exist" and thus generating erroneous or absurd outputs. It is the model's seemingly reasonable but actually wrong overconfident output.
Latency (Latency)
The time it takes for the model to output results after receiving a request. It is one of the most common engineering jargon terms and frequently appears in discussions about implementation and productization.
Guardrails (Guardrails)
Used to restrict what the model/Agent can do, when to stop, and what content cannot be output.
Vibe Coding (Vibe Coding)
This term is also one of the most popular AI jargons today. It means users directly express requirements through conversation, and the AI writes the code, while the user doesn't need to specifically understand how to code.
Parameters (Parameters)
The internal numerical scale of the model used to store capabilities and knowledge. It is often used as a rough measure of the model's size. "Billions of parameters" and "trillions of parameters" are among the most common intimidating phrases in the AI circle.
Reasoning Model (Strong Reasoning Model)
It typically refers to models that are better at multi-step reasoning, planning, verification, and complex task execution.
MCP (Model Context Protocol)
This is a very hot new jargon in the past year. Its role is similar to establishing a universal interface between the model and external tools/data sources.
Fine-tuning / Tuning (Fine-tuning)
Continuing training on a base model to make it more adapted to specific tasks, styles, or domains. Google's terminology list directly treats tuning and fine-tuning as related concepts.
Distillation (Distillation)
Compressing the capabilities of a large model into a smaller model as much as possible, akin to having a "teacher" teach a "student."
RAG (Retrieval-Augmented Generation)
This has almost become a basic configuration for enterprise AI. Microsoft defines it as a "search + LLM" pattern, using external data to ground the answers, solving problems like outdated training data and lack of knowledge about private knowledge bases. The goal is to base answers on real documents and private knowledge, rather than relying solely on the model's own memory.
Grounding (Fact Alignment)
Often appears together with RAG. It means making the model's answers based on external sources like documents, databases, web pages, etc., rather than just relying on parameter memory for "free play." Microsoft explicitly lists grounding as a core value in its RAG documentation.
Embedding (Vector Embedding / Semantic Vector)
Encoding text, images, audio, and other content into high-dimensional numerical vectors to calculate semantic similarity.
Benchmark (Benchmark Test)
A evaluation method that uses a unified set of standards to test model capabilities. It is also the language of leaderboards that various vendors love to use to "prove they are strong."
