Glossary: AI terms every content creator should know
You’re probably seeing “AI” everywhere these days. It’s much more than a buzzword: AI tools and technology are massively reshaping how people work, study and communicate.
We know a lot of people are newer to AI, so our team put together this handy glossary. Learn the definitions for common terms you’ll see as you explore AI content creation and workflows.
Top 20 AI vocabulary terms to know
AI: Artificial Intelligence, or AI, is a blanket term for technologies that can automate decisions, predict outcomes or provide generated output. AI is applied across industries and use cases, so you’ll often encounter specialized terms for a specific type of AI.
AI avatars: Avatars are customizable, AI-generated characters that you can use for generated images or video. You use the AI avatar generator to produce a character that fits your needs, then customize details from there. People might also refer to avatars as “AI actors” or “AI influencers”.
AI credits: AI credits serve as currency for many AI tools and platforms. Depending on which features you use and how the company chooses to charge for them, AI credits correspond to using specific AI features. Often, your subscription tier or payment plan determines how many AI credits you get per month.
AIGC: AI generated content (AIGC) refers to content that’s made with the help of AI. It includes multiple kinds of content, from text to video to audio. Usually, people use AIGC to talk about content fully-generated by AI, with minimal human involvement.
AI twin: This specifically refers to someone using a photo or existing footage to “clone” their likeness and produce a digitized AI avatar. AI generators turn the existing asset into a realistic avatar that can be used for future content creation.
AI UGC: A specific type of AIGC, where people use AI tools to make content that looks like user-generated content. It’s often used to talk about content that uses AI avatars or influencers.
AI video: A broad category, this technically can include any kind of video that uses AI as part of the production process. Sometimes the entire video is made with an AI video generator, while other videos simply use AI editing features or smaller components built with AI.
AI video generator: These tools generate clips based on someone’s prompt, reference or topic. The generator produces footage using AI, automating many parts of the traditional production process.
Chatbots: Chatbots depend on conversational AI. They speak to you like it’s a natural conversation, using AI to respond to your messages.
Conversational AI: Conversational AI uses machine learning and natural language processing (NLP) to process and respond to text or speech, allowing for human-like interactive dialogue. It powers tools like chatbots, virtual agents and popular tools like ChatGPT. You type or talk to communicate your need or idea, and the AI responds with context-aware answers.
Deepfake: Deepfakes use AI to create content depicting events that didn't happen. It’s often in the context of impersonating a real individual.
Enterprise AI: Tools or systems designed to implement AI across a large workforce or organization. Enterprise AI can cover a broad range of use cases for different teams. It’s commonly used to automate work, improve existing processes or optimize outputs.
Foundation model: AI models trained on massive datasets to perform specific tasks. The training data varies depending on the model’s intended purpose. For example, LLMs are one type of foundation model, trained on text and code.
Generative AI: Generative AI creates new content based on a user’s prompts or inputs. Depending on the tool, it may produce content like videos, images or text. You can typically tailor specific variables and settings to customize content.
Knowledge base: Many AI tools use knowledge bases to store information that’s used across prompts and tasks. For example, you could store brand guidelines, product info or other material that you’ll want to reference over time.
LLM: Large language models, or LLMs, are trained on datasets of text and code. Once they’re trained, they can generate responses based on common language patterns or knowledge. For example, chatbots often use LLMs to power their responses.
Machine learning: Machine learning refers to algorithms that learn and evolve over time, without direct programming updates.
Multimodal model: Multimodal models are one kind of foundation model. They’re trained on multiple kinds of inputs, like images, audio and text. Because they draw from multiple sources, they can power more comprehensive content creation.
Prompt: Instructions that you write for an AI system to inform generated results. Tailoring your prompt can change the outcome, so people often test different versions to see their favorite results.
Voice cloning: With help from a reference audio sample, audio models can reproduce someone’s voice as a digital clone. The clone can then be used for future content.
Start using AI in your video workflows
At Mirage, we’re focused on making video easier for everyone. We build in-house foundation models and tools that redefine how video gets made for top social platforms and advertising formats.
Tools like Captions are designed for all, no matter your video skills or experience with other AI tools. Whether you want to go all-in on generative AI or just want to try some AI features, Captions can fit into your existing workflows and help your team move faster.
