AI conversations are full of confusing terminology. LLMs, tokens, hallucinations, RAG, fine-tuning, embeddings. It feels like everyone’s speaking another language.

This glossary explains every AI term you’re likely to encounter, in plain English, with practical context for how each concept affects you as a user. No computer science degree required.

Bookmark this page. You’ll need it.

Core AI Concepts

Artificial Intelligence (AI)

The broad field of creating machines that can perform tasks typically requiring human intelligence (understanding language, recognizing images, making decisions, solving problems).

In everyday conversation, “AI” usually refers to the new generation of tools like ChatGPT, not robots or science fiction scenarios.

Machine Learning (ML)

A subset of AI where systems learn from data rather than being explicitly programmed. Instead of writing rules for every situation, developers feed the system examples and it finds patterns.

When someone says an AI was “trained,” they’re talking about machine learning.

Deep Learning

A type of machine learning using neural networks with many layers (hence “deep”). This approach powers most modern AI: image recognition, language understanding, voice assistants.

You don’t need to understand the technical details. Just know it’s the underlying technology making today’s AI breakthroughs possible.

Neural Network

A computing system loosely inspired by the human brain. It consists of interconnected nodes (like neurons) that process information in layers.

Think of it as the architecture that allows AI to find complex patterns in data.

Training

The process of teaching an AI system by exposing it to large amounts of data. During training, the AI adjusts its internal parameters to get better at its task.

When someone says a model was “trained on the internet,” they mean it learned from massive amounts of text data.

Inference

When an AI applies what it learned to new inputs. Training is the learning phase; inference is the using phase.

Every time you chat with ChatGPT, that’s inference. The model is using what it learned to respond to you.

Language Models

Large Language Model (LLM)

An AI trained on massive amounts of text that can understand and generate human language. ChatGPT, Claude, Llama, and Gemini are all LLMs.

“Large” refers to both the training data (billions of words) and the model size (billions of parameters).

GPT (Generative Pre-trained Transformer)

A specific type of language model architecture developed by OpenAI. GPT models are trained to predict the next word in a sequence.

ChatGPT uses GPT models. The name has become somewhat generic. People sometimes use “GPT” to mean any similar model.

Transformer

A neural network architecture that revolutionized natural language processing. It’s especially good at understanding context and relationships in text.

The “T” in GPT, BERT, and other model names stands for Transformer.

Parameters

The internal values that a neural network adjusts during training. A model with more parameters can potentially learn more complex patterns.

When you hear “GPT-4 has over a trillion parameters,” that’s describing the model’s size and complexity.

Context Window

How much text an AI can consider at once. If a model has an 8,000-token context window, it can only see about 6,000 words of conversation at a time.

Larger context windows mean the AI can handle longer documents and maintain context in extended conversations.

Token

A unit of text that AI processes, roughly ¾ of a word in English. “Artificial intelligence” might be 3-4 tokens.

Tokens matter because AI pricing and limits are often based on token count.

AI Behaviors and Outputs

Hallucination

When an AI generates information that sounds plausible but is factually incorrect or completely made up. It’s not lying. It’s generating text that fits patterns without verifying facts.

Always verify important information from AI, especially specific facts, numbers, and citations.

Prompt

The input you give to an AI: your question, instruction, or request. The quality of your prompt significantly affects the quality of the response.

Response / Completion

The AI’s output, or what it generates in reply to your prompt.

Temperature

A setting that controls randomness in AI responses. Higher temperature means more creative and varied outputs; lower temperature means more consistent and predictable.

Most users don’t adjust this directly, but it’s why the same prompt can give slightly different answers.

Alignment

How well an AI’s behavior matches human intentions and values. An “aligned” AI does what humans actually want, not just what they literally ask for.

This is what AI safety researchers work on: making sure powerful AI behaves helpfully and safely.

Training and Customization

Pre-training

The initial training phase where an AI learns general knowledge from massive datasets. This gives it broad capabilities before any specialization.

Fine-tuning

Additional training on specific data to specialize a model for particular tasks or domains. For example, a general model might be fine-tuned on medical texts to become better at healthcare questions.

RLHF (Reinforcement Learning from Human Feedback)

A training technique where humans rate AI outputs, and the model learns from those ratings. This helps AI give responses that humans find more helpful and appropriate.

ChatGPT and Claude both use RLHF to improve their responses. Anthropic has been particularly innovative in this area.

LoRA (Low-Rank Adaptation)

A efficient fine-tuning method that adds small trainable pieces to a model without changing the whole thing. Popular in the Stable Diffusion community for adding styles or concepts.

Embedding

A way of representing text (or images) as numbers that capture meaning. Similar concepts have similar embeddings.

This is how AI can understand that “happy” and “joyful” are related even though the words look different.

Vector Database

A database optimized for storing and searching embeddings. Used for semantic search, which finds information based on meaning rather than exact keywords.

Types of AI Systems

Generative AI

AI that creates new content (text, images, music, video). ChatGPT generating text and Midjourney generating images are both generative AI.

Multimodal AI

AI that works with multiple types of data like text, images, and audio. GPT-4V can see images and discuss them. Some models can generate both text and images.

Diffusion Model

A type of generative AI that creates images by starting with noise and gradually refining it. Stable Diffusion, DALL-E, and Midjourney all use diffusion models.

Foundation Model

A large AI model trained on broad data that can be adapted to many tasks. GPT-4, Claude, and Llama are foundation models. They’re not built for just one thing.

Open Source / Open Weight

AI models where the code and/or trained weights are publicly available. Anyone can download and use them. Llama and Stable Diffusion are open. GPT-4 and Claude are not.

Closed Source / Proprietary

AI models that are only available through the company’s own services. You can use them but can’t download or modify them.

Usage and Access

API (Application Programming Interface)

A way for software to communicate with AI services. Developers use APIs to build apps powered by AI (the app sends requests to the AI and receives responses).

If you’re just using ChatGPT in a browser, you’re not using the API. But apps you use might be.

Endpoint

The specific URL or address where an API can be reached. Different endpoints might offer different models or capabilities.

Rate Limit

Restrictions on how quickly or how much you can use an AI service. Prevents any one user from overloading the system.

Latency

The time delay between sending a request and receiving a response. Lower latency means faster responses.

Streaming

Receiving AI responses word by word as they’re generated, rather than waiting for the complete response. This is why ChatGPT responses appear gradually.

Safety and Ethics

Content Moderation

Systems that filter AI inputs and outputs to prevent harmful content. Why AI will refuse certain requests.

Jailbreaking

Attempting to bypass AI safety measures through clever prompting. Generally against terms of service and can result in account restrictions.

Bias

Systematic unfairness in AI outputs, often reflecting biases in training data. AI can perpetuate stereotypes or treat groups differently.

Guardrails

Built-in limits on what an AI will do. These prevent the AI from helping with harmful tasks or generating inappropriate content.

Infrastructure

GPU (Graphics Processing Unit)

The hardware that AI runs on. GPUs are much faster than regular CPUs for AI tasks because they can do many calculations simultaneously.

Cloud AI

AI services running on remote servers. You access them over the internet with no special hardware needed.

Local AI / On-device AI

AI running on your own computer or phone. Requires appropriate hardware but offers privacy and no internet dependency.

Quantization

Reducing the precision of model numbers to make them smaller and faster. A quantized model might be 4-bit instead of 16-bit. It’s less accurate but more efficient.

This is how large models can run on consumer hardware.

2026 Terminology

Agent

An AI system that can take autonomous actions like browsing the web, running code, and managing files, not just generate text. Agents combine language models with tools and can complete multi-step tasks.

Practical impact: Agents can automate workflows that previously required human intervention.

RAG (Retrieval-Augmented Generation)

A technique that combines AI generation with information retrieval. The AI searches a knowledge base (documents or databases) before generating answers. This reduces hallucinations and enables up-to-date responses.

Practical impact: This is how AI chatbots answer questions about company documents or recent events they weren’t trained on.

Prompt Engineering

The practice of writing effective prompts to get better AI outputs. Includes techniques like providing examples, specifying formats, breaking down complex tasks, and giving the AI a role to play.

Practical impact: Good prompts can dramatically improve AI output quality without any technical changes.

Constitutional AI

A training approach where AI is taught to evaluate its own outputs against a set of principles (a “constitution”). Used to make AI more helpful, harmless, and honest.

Practical impact: Why modern AI assistants are generally safer and more aligned with user intent than earlier versions.

Chain of Thought

A prompting technique that asks AI to “think step by step” or show its reasoning process. Often improves accuracy on complex problems.

Practical impact: If AI gives a wrong answer, asking it to explain its reasoning step by step often produces a better result.

System Prompt

Hidden instructions that set up how an AI assistant behaves. When you use ChatGPT or Claude, there’s a system prompt behind the scenes defining its personality and rules.

Practical impact: You don’t see system prompts in consumer apps, but they shape how AI responds to you.

Context Length / Context Window

How much text an AI can see at once, measured in tokens. Longer context means it can handle longer documents or conversations without forgetting earlier parts.

Practical impact: If an AI forgets what you said earlier in a conversation, you’ve exceeded its context window.

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Quick Reference Tables

Model Comparison Terms

Term	Meaning	Why It Matters
Parameters	Size of the model	More means generally more capable
Context window	Memory capacity	Longer means handles more text
Latency	Response time	Lower means faster replies
Throughput	Requests handled	Higher means more scalable

Cost-Related Terms

Term	Meaning	Example
Token	Unit of text	”Hello” = 1 token
Input tokens	What you send	Your prompt
Output tokens	What AI generates	The response
Per-million pricing	Cost per 1M tokens	$3/1M tokens

Quality-Related Terms

Term	Good Sign	Bad Sign
Accuracy	Correct information	Hallucinations
Coherence	Logical flow	Contradictions
Helpfulness	Solves your problem	Refuses or deflects
Harmlessness	Safe responses	Harmful content

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Frequently Asked Questions About AI Terms

What’s the difference between AI, ML, and deep learning?

AI is the broadest term (any computer system doing smart things). Machine learning is AI that learns from data. Deep learning is machine learning using neural networks with many layers. Deep learning powers most modern AI tools like ChatGPT.

Why do AI companies use so much jargon?

Partly technical necessity (these are real computer science concepts), partly marketing (jargon makes things sound more sophisticated). The core ideas aren’t that complicated; the jargon just obscures them.

What term should I learn first?

Start with: prompt (what you ask), token (how text is measured), hallucination (when AI makes things up), and context window (how much it can remember). These four concepts explain most practical AI behavior.

Is there a difference between “model” and “AI”?

Yes. A model is the trained system (like GPT-4). AI is broader and includes the model plus all the infrastructure around it (the interface, safety systems, etc.). ChatGPT is the product; GPT-4 is the model inside it. For more on different models, see our AI models explained guide.

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Putting It Together

You don’t need to memorize all these terms. But having a reference helps when you encounter them in articles, product descriptions, or conversations about AI tools.

The core concepts (models, training, prompts, tokens, hallucinations, context) remain relevant even as specific implementations change. New terms emerge constantly, but they typically build on these foundations.

Understanding the vocabulary helps you:

• Evaluate AI tool claims more critically
• Make better decisions about which tools to use
• Communicate more effectively with technical colleagues
• Understand AI limitations and capabilities

When you encounter a term not listed here, most AI assistants can explain it. Just ask “What is [term] in plain English?”

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Last updated: February 2026. New AI terms emerge regularly. This glossary covers the most commonly encountered terminology.