AI Glossary
47 essential AI terms explained in plain language. From machine learning basics to the latest in generative AI.
A
AI Agent
An AI system that can autonomously plan and execute multi-step tasks to achieve a goal. Unlike simple chatbots that respond to single queries, agents can use tools, browse the web, write code, and take actions. They represent a step toward more autonomous AI systems.
AI Avatar
A digital representation of a person created using artificial intelligence. AI avatars can be static images or animated video characters that speak and move realistically. They are used for video presentations, customer service, social media content, and virtual meetings.
AI Detection
Technology designed to identify whether content was generated by an AI model rather than a human. AI detection tools analyze writing patterns, statistical distributions, and other signals to flag machine-generated text. Used by educators, publishers, and content platforms to verify content authenticity.
API (Application Programming Interface)
A set of protocols that allows different software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications — for example, using OpenAI's API to add text generation to an app without building a model from scratch.
Artificial Intelligence (AI)
A broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. This includes learning from data, recognizing patterns, making decisions, and understanding natural language. Modern AI encompasses machine learning, deep learning, and generative AI approaches.
Attention Mechanism
A technique in neural networks that allows the model to focus on the most relevant parts of the input when producing output. Self-attention enables transformers to weigh the importance of different words in a sentence relative to each other, dramatically improving language understanding.
Automation
Using technology to perform tasks with minimal human intervention. AI automation goes beyond simple rule-based workflows by understanding context, making decisions, and adapting to changing conditions. Common applications include email automation, data processing, content scheduling, and business process automation.
C
Chatbot
An AI-powered conversational interface that can understand and respond to user messages in natural language. Modern chatbots are typically powered by large language models and can answer questions, assist with tasks, generate content, and hold multi-turn conversations.
Computer Vision
A field of AI that enables computers to interpret and understand visual information from images and videos. Applications include object detection, facial recognition, image classification, OCR (optical character recognition), and visual quality inspection.
Content Optimization
The process of improving content to better serve its intended purpose — whether for search rankings, user engagement, conversions, or accessibility. AI content optimization tools analyze text for readability, keyword usage, semantic relevance, and competitive positioning.
Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single interaction. Larger context windows allow models to handle longer documents and conversations. Context windows have grown from 4K tokens in early GPT models to 200K+ in models like Claude.
Copywriting
The craft of writing persuasive text for marketing and advertising purposes. AI copywriting tools can generate ad copy, social media posts, email campaigns, product descriptions, and landing page content. They help marketers produce more content faster while maintaining brand voice.
D
Deep Learning
A subset of machine learning that uses artificial neural networks with multiple layers (hence 'deep') to model complex patterns in data. Deep learning powers most modern AI breakthroughs including image recognition, natural language processing, and generative AI.
Diffusion Model
A type of generative AI model that creates data by learning to reverse a gradual noising process. Starting from pure noise, the model iteratively removes noise to generate realistic images, audio, or video. Stable Diffusion, DALL-E 3, and Midjourney all use diffusion-based approaches.
E
F
Few-Shot Learning
A machine learning approach where a model learns to perform a task from just a few examples, rather than requiring thousands of training samples. In the context of LLMs, few-shot learning means providing a few examples in the prompt to guide the model's behavior for a specific task.
Fine-Tuning
The process of further training a pre-trained AI model on a specific dataset to specialize it for a particular task or domain. Fine-tuning allows companies to customize general-purpose models for their specific needs while requiring much less data and compute than training from scratch.
Freemium
A pricing model where a basic version of the product is available for free, with premium features requiring a paid subscription. Most AI tools use a freemium model — offering limited usage for free to attract users, then charging for higher limits, advanced features, or commercial use.
G
Generative AI
AI systems that can create new content — text, images, audio, video, or code — rather than simply analyzing existing data. Generative AI models learn patterns from training data and produce original outputs. Examples include ChatGPT for text, DALL-E for images, and Suno for music.
Grounding
Techniques used to connect AI model outputs to verified, factual information sources. Grounding reduces hallucinations by having the model reference real documents, databases, or search results rather than relying solely on its training data. RAG is a common grounding approach.
H
I
L
M
Machine Learning (ML)
A subset of artificial intelligence where systems learn and improve from experience without being explicitly programmed. ML algorithms build mathematical models based on training data to make predictions or decisions. Common approaches include supervised learning, unsupervised learning, and reinforcement learning.
Multimodal AI
AI systems that can process and generate multiple types of data — text, images, audio, and video — within a single model. Multimodal models like GPT-4o can analyze images, generate text, understand speech, and produce audio responses in a unified conversation.
N
Natural Language Processing (NLP)
A branch of AI that enables computers to understand, interpret, and generate human language. NLP powers chatbots, language translation, sentiment analysis, text summarization, and content generation tools. Modern NLP is largely based on transformer architectures.
Neural Network
A computing system inspired by the biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information. Neural networks are the foundation of deep learning and can learn to recognize patterns, classify data, and generate new content.
No-Code
A development approach that allows people to build applications, workflows, and automations without writing traditional code. No-code platforms use visual interfaces, drag-and-drop builders, and pre-built components. AI-powered no-code tools make it even easier to create sophisticated applications.
O
P
Paraphrasing
Rewriting text to express the same meaning using different words and sentence structures. AI paraphrasing tools can rephrase content to improve clarity, change tone, simplify language, or make text more original. They are used by writers, students, and content teams.
Prompt
The input text or instruction given to an AI model to guide its output. Effective prompts clearly describe the desired result, provide context, and may include examples. The quality of a prompt significantly affects the quality of the AI's response.
Prompt Engineering
The practice of crafting and optimizing prompts to get better results from AI models. Techniques include providing clear instructions, using few-shot examples, chain-of-thought reasoning, and specifying the desired output format. It has become a key skill for effectively using AI tools.
R
S
SaaS (Software as a Service)
A software delivery model where applications are hosted in the cloud and accessed via a web browser. Most AI tools operate as SaaS products with subscription-based pricing. This model allows users to access powerful AI capabilities without managing infrastructure or installing software.
Sentiment Analysis
An NLP technique that determines the emotional tone or attitude expressed in text — positive, negative, or neutral. Businesses use sentiment analysis to monitor brand perception, analyze customer feedback, track social media reactions, and understand market sentiment.
SEO (Search Engine Optimization)
The practice of optimizing websites and content to rank higher in search engine results. AI-powered SEO tools help with keyword research, content optimization, competitor analysis, technical audits, link building strategies, and performance tracking.
Speech-to-Text (STT)
AI technology that converts spoken audio into written text. Also known as automatic speech recognition (ASR). Modern STT systems can handle multiple languages, accents, background noise, and speaker diarization. Widely used for transcription, subtitling, and voice interfaces.
T
Text-to-Image
AI technology that generates images from text descriptions (prompts). Models like DALL-E, Midjourney, and Stable Diffusion create realistic or artistic images by understanding the semantic meaning of text and translating it into visual content using diffusion or other generative techniques.
Text-to-Speech (TTS)
AI technology that converts written text into natural-sounding spoken audio. Modern TTS systems produce highly realistic voices with appropriate intonation, emotion, and pacing. They are used for voiceovers, audiobook narration, accessibility features, and content creation.
Text-to-Video
AI technology that generates video content from text descriptions or scripts. These tools can create animated explainers, marketing videos, social media content, and even realistic footage. The technology has rapidly advanced with models like Sora, Runway, and Pika.
Token
The basic unit of text that language models process. A token can be a word, part of a word, or a punctuation mark. For example, 'unhappiness' might be split into 'un', 'happiness'. Most AI APIs price usage per token, with typical costs measured per 1,000 or 1 million tokens.
Training Data
The dataset used to teach an AI model. For language models, this includes text from books, websites, and other sources. For image generators, it includes image-text pairs. The quality, diversity, and size of training data directly impact a model's capabilities and potential biases.
Transcription
The process of converting audio or video content into written text. AI transcription tools offer near-real-time accuracy across multiple languages, with features like speaker identification, timestamps, and automatic punctuation. Used for meetings, interviews, podcasts, and video content.
Transfer Learning
A machine learning technique where a model trained on one task is repurposed for a different but related task. For example, a model trained on general text can be fine-tuned for medical writing. Transfer learning dramatically reduces the data and compute needed to build specialized AI applications.
Transformer
A neural network architecture introduced in the 2017 paper 'Attention Is All You Need.' Transformers use self-attention mechanisms to process input data in parallel, making them highly efficient for language tasks. They are the foundation of models like GPT, BERT, and Claude.