Multimodal search and Retrieval-Augmented Generation (RAG) have practical applications across industries by enhancing how systems process and generate information from diverse data types. Multimodal search allows querying across text, images, audio, and video, while RAG improves generative models by grounding responses in retrieved data. Together, they enable more accurate, context-aware solutions in areas like e-commerce, healthcare, and customer support, where combining multiple data sources or generating reliable outputs is critical.
One key application of multimodal search is in e-commerce platforms. For example, users can upload a photo of a product they want to find, and the system matches it with similar items in the catalog using visual features, text descriptions, or even user reviews. Retailers like Amazon or eBay use this to improve product discovery, letting shoppers search using images instead of keywords. In healthcare, multimodal search helps doctors cross-reference medical images (like X-rays) with patient records or research papers. A radiologist could query a database for similar cases by uploading a scan, accelerating diagnosis by surfacing relevant historical data. Media companies also use it to index video content—searching for specific scenes using text (e.g., “car chase in rain”) by analyzing both audio transcripts and visual frames.
RAG, on the other hand, is widely used in chatbots and knowledge management. Customer support systems, like those used by banks or telecom companies, employ RAG to pull answers from internal documentation or FAQs, then generate clear, up-to-date responses. This reduces reliance on static pre-trained knowledge, ensuring accuracy as policies change. Developers also apply RAG in research tools—for instance, a tool that retrieves snippets from academic papers when asked a question like “What’s the latest on quantum computing?” and generates a summary. In legal tech, RAG can draft contracts by retrieving clauses from existing templates and adapting them to user inputs. Combining multimodal search with RAG opens further possibilities: a travel app might retrieve images of landmarks and hotel reviews, then generate a personalized itinerary using both data types. These technologies solve real problems by making information retrieval and generation more dynamic and context-aware.