Multimodal
Multimodal AI can understand and generate more than one type of content — text, images, audio, and video — letting engines answer questions that span formats.
Multimodal means the AI handles more than text. A multimodal model can take in and reason over images, audio, and video alongside words — so a user can show it a photo, play it a clip, or ask about a chart, and it can respond. Engines like Gemini are built this way.
For AEO, multimodality expands what's citable but doesn't change the core rule that machines reward clarity. Images, audio, and video are far more useful to an engine when paired with descriptive text — alt text, transcripts, captions — that makes their content extractable. A video with a clean transcript can be quoted; a silent, unlabeled image often can't. Multimodal embeddings let engines match a query to the right piece of visual or audio content.
Example. A recipe video with a full transcript and step text can be surfaced when someone asks "how do I fold dumplings," because the engine can read the steps — while the same video with no transcript stays largely invisible to text-based retrieval.
Relevant pillar
Related terms
- GeminiGemini is Google's family of multimodal AI models and its consumer assistant, able to reason over text, images, and more, and to ground answers in retrieved sources.
- EmbeddingsEmbeddings are numerical representations of text that capture its meaning, letting AI systems find passages that are semantically related to a query even when they share no exact keywords.
- Large Language Model (LLM)A large language model is an AI system trained on vast amounts of text to predict and generate language, and is the engine that writes the answers in AI search.