USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO RECOMMEND SUPPORTING MATERIAL FOR MEDIA PROJECTS

A large language model (LLM) is used to broaden a search for supporting material for a media project. The LLM is provided with contextual material from the media project and optional grounding material and generates a list of types of supporting material. The list is provided to a machine-learning-based encoder, which encodes the list items into embedding space vectors. A body of source material, which may multimodal, is also encoded into the embedding space. A search engine is used to locate items of source material having embedded space vectors closest to vectors corresponding to the list of types of supporting material. Located items are provided to a media editing application as suggested supporting material. The body of source material may include live external sources. A small language model fine-tuned with a training data set generated by a LLM may be used instead of the LLM.