Question 1

What is multimodal RAG in artificial intelligence?

Accepted Answer

Multimodal RAG (Retrieval-Augmented Generation) is an AI approach that retrieves and analyzes information from multiple data formats such as text, images, audio, videos, and structured data to generate accurate, context-aware responses.

Question 2

How do AI agents work with multimodal RAG systems?

Accepted Answer

AI agents act as intelligent coordinators that identify user intent, retrieve relevant data from different formats, align insights across sources, and generate meaningful responses using multimodal RAG systems.

Question 3

Why are multimodal RAG systems important for enterprises?

Accepted Answer

Multimodal RAG systems help enterprises unify large volumes of structured and unstructured data, enabling faster decision-making, improved analytics, and scalable enterprise AI solutions.

Question 4

How is multimodal RAG used in healthcare?

Accepted Answer

In healthcare, multimodal RAG systems analyze medical reports, imaging scans, lab results, and patient histories together to create unified summaries that support faster diagnoses and informed clinical decisions.

Question 5

Can multimodal RAG improve customer support systems?

Accepted Answer

Yes, multimodal RAG improves customer support by combining product manuals, images, tutorial videos, and reviews to deliver accurate, step-by-step answers that enhance customer satisfaction.

Question 6

What are the challenges of implementing multimodal RAG systems?

Accepted Answer

Key challenges include interpreting multiple data formats accurately, ensuring data privacy and security, managing bias, and maintaining human oversight, especially in sensitive domains like healthcare and legal services.

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