Glossary: 33 AI terms every content creator should know
Last updated: July 30, 2026
AI is reshaping how video gets made, distributed, and found. But the terminology moves fast, and understanding what different tools actually do separates people who use AI effectively from those who get sold on buzzwords.
This glossary covers 33 AI terms every content creator should know, from foundational concepts to specific video, audio, and content generation terms you’ll encounter in marketing and creative workflows.
Foundational terms and AI models
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Artificial intelligence (AI): 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.
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Deep learning: A subset of machine learning that uses neural networks with many layers (hence “deep”) to learn patterns from large datasets. Most modern AI video, image, and language tools are built on deep learning. The “layers” allow the model to learn increasingly abstract representations of the input.
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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.
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Generative AI (Gen AI): Generative AI creates new content based on a user’s prompts or inputs. It’s “generative” because it produces new outputs rather than simply classifying or retrieving existing information. Depending on the tool, it may produce content like videos, images, or text.
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LLM (Large Language Model): 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.
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Machine learning: Machine learning refers to algorithms that learn and evolve over time, without direct programming updates. A machine learning model improves its outputs by processing examples rather than by following explicitly coded rules.
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Multimodal model: Multimodal models are a type 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.
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Training data: The dataset used to teach an AI model how to perform its task. The quality, scale, and diversity of training data largely determine output quality.
AI video and visual content
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AI avatars: Avatars are customizable, AI-generated actors that you can use for images or video. Use an AI avatar generator to make a character that fits your needs.
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AI caption generator: A tool that transcribes a video’s spoken audio and generates synced captions . Captions are automatically timed to the corresponding audio.
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AIGC (AI-generated content): Usually describes content fully generated by AI with minimal human involvement. When people say “AI-assisted content,” they typically mean that a human made the primary creative decisions.
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AI twin: This specifically refers to someone using a photo or existing footage to “clone” their likeness and produce a digitized AI avatar. AI turns the existing asset into a realistic avatar that can be used for future content.
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AI UGC: A specific type of AIGC, where people use AI to make content styled like user-generated content . It often involves AI avatars and is sometimes called “UGC-style content.”
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AI video: A broad category that includes any video where AI played a part in the production process. Sometimes the entire video is made with an AI video generator, while other videos use smaller AI components or AI editing .
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AI video generator: Video generators automate several parts of the traditional production process, generating clips based on prompts, references, or topics.
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Deepfake: Deepfakes use AI to create content depicting events that didn’t happen. They’re often used to impersonate a real individual.
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Image-to-video: A generation approach where a still image is used as the starting frame, and AI generates motion from that reference point.
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Synthetic media: Media that is artificially generated or manipulated using AI, rather than captured or created by humans directly. Distinguished from deepfakes by context: synthetic media created with consent and transparency is legitimate; synthetic media created to deceive is a deepfake.
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Text-to-edit: A type of video or image editing where you use plain text prompts to describe changes you want to make.
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Text-to-video: A category of AI generation where a text prompt is the primary input and video footage is the output. The model interprets the description and generates matching video. You may also see this called “prompt-to-video”.
AI audio and dubbing
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AI dubbing: The process of replacing the audio in a video with audio in a new language.
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Lip sync AI: AI technology that adjusts the visible mouth movements in a video to match a different audio track. Used primarily in AI dubbing , lip sync ensures the speaker’s mouth movements correspond to the translated words.
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Text-to-speech (TTS): AI technology that converts written text into spoken audio. Modern neural TTS has advanced significantly beyond robotic-sounding voices and produces natural-sounding speech with emotional inflection. Used in AI voiceover , AI dubbing , and accessibility applications.
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Voice cloning: With help from a reference audio sample, audio models can reproduce someone’s voice as a digital clone. The clone preserves vocal characteristics (pitch, cadence, accent, tone) and can be used for new content.
Prompting and inputs
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Agentic AI: AI systems that can perform sequences of actions autonomously rather than simply responding to a single prompt. An agentic AI video workflow might automatically research a topic, draft a script, generate footage and schedule posting without human intervention at each step.
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Context window: The amount of text that an AI model can “see” and consider at once when generating a response. Context is typically measured in tokens, and a larger context window allows the model to maintain coherence across longer documents, conversations, or video scripts.
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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 and virtual agents.
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Hallucination: When an AI model generates output that is factually incorrect, but stated confidently. LLMs hallucinate because they generate statistically likely text rather than retrieving verified facts. For creators using AI for research or factual content, hallucination is the primary quality risk. Always verify AI-generated claims against primary sources before publishing.
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Human-in-the-loop: A design principle where a human reviews, approves, or adjusts AI outputs before they’re finalized or acted upon. Human-in-the-loop workflows balance the speed of AI with the judgment of a human.
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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.
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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.
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Prompt engineering: The practice of crafting, testing, and refining prompts to consistently get better outputs from AI models. Effective prompt engineering requires understanding how the model interprets input and structuring requests accordingly. For video creation, prompt engineering involves learning which descriptors affect footage the most .
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Token: The basic unit of text that AI language models process. Tokens are chunks of characters that the model’s tokenizer has learned to recognize. Longer prompts and responses consume more tokens, which affects both cost and context window limits.
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. Tools like Captions are designed for everyone, no matter your video skills or AI experience. Whether you want to go all-in on generative AI or just want to try some small AI features to start, Captions can help you move faster.
