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Plain-language AI glossary

Term 67TechniquesMeaning / context / connections

Techniques / Definition

Multimodal RAG

An extension of RAG that retrieves and reasons over multiple data types: text, images, tables, charts, and audio: not just text documents.

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MeaningThe one-sentence definition.

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ContextHow the idea works in practice.

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01 / Plain-language context

How Multimodal RAG works.

Multimodal RAG extends retrieval beyond plain text to documents containing images, tables, charts, diagrams, audio, or video. A system may parse media into text, store multimodal embeddings, retrieve original assets, or pass both the asset and its extracted structure to a model.

The best approach depends on what must be preserved. Converting a chart to text can lose visual relationships, while sending every full-resolution asset can be expensive and noisy. Evaluate retrieval quality, citation traceability, accessibility, privacy, and the model's ability to interpret the actual media rather than choosing an architecture by fashion.

02 / Practical uses

Where it helps.

  1. 01Financial document analysis with charts
  2. 02Medical record processing
  3. 03Product catalog search
  4. 04Technical documentation with diagrams
  5. 05Scientific paper analysis
03 / Keep exploring
RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Multimodal AIAI models that can process and generate multiple types of data: text, images, audio, video, and code.EmbeddingsNumerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.Computer VisionThe field of AI that enables machines to interpret and understand visual information from images and video.Vector DatabaseA specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.