What you'll learn:
- You are going to learn about Retrieval-Augmented Generation with multimodal data
- You are going to build multimodal Retrieval-Augmented Generation systems
- You are going to multi multimodal search
- You are going to build multi-vector recommended system
Retrieval-Augmented Generation is a hybrid model that integrates retrieval mechanisms with generative models, enhancing the ability of AI to generate more accurate and contextually relevant text. RAG combines the strengths of information retrieval systems, such as search engines, with the language generation capabilities of models. This approach addresses a common limitation in generative models: the challenge of producing factual and up-to-date information.
Retrieval-Augmented Generation overcomes this by introducing a retrieval component that fetches relevant documents from an external corpus, often using dense retrievers such as DPR (Dense Passage Retrieval) or BM25, during the generation process. outputs are produced based on a static dataset on which the model has been trained. While this allows for coherent text generation, these models often struggle with generating factually accurate or domain-specific responses, especially when the required information was not part of their training data.
Retrieval-Augmented Generation enhances the performance of generative models by integrating retrieval systems, making it a powerful tool for producing accurate, contextually relevant, and real-time information in various AI-driven applications. One of the significant advantages of Retrieval-Augmented Generation is its flexibility in incorporating external knowledge sources, such as databases, research papers, or updated web articles. This makes it particularly effective for applications requiring real-time, factual information, such as question-answering systems, customer support, or technical documentation.
