Use cases for Retrieval Augmented Generation

The following are common use cases for using a RAG approach:

• Search engines – RAG-enabled search engines can provide more accurate and up-to-date featured snippets in their search results.

• Question-answering systems – RAG can improve the quality of responses in question-answering systems. The retrieval-based model uses similarity search to find relevant passages or documents that contain the answer. Then, it generates a concise and relevant response based on that information.

• Retail or e-commerce – RAG can enhance the user experience in e-commerce by providing more relevant and personalized product recommendations. By retrieving and incorporating information about user preferences and product details, RAG can generate more accurate and helpful recommendations for customers.

• Industrial or manufacturing – In manufacturing, RAG helps you quickly access critical information, such as factory plant operations. It can also help with decision-making processes, troubleshooting, and organizational innovation. For manufacturers who operate within stringent regulatory frameworks, RAG can swiftly retrieve updated regulations and compliance standards from internal and external sources, such as from industry standards or regulatory agencies.

• Healthcare – RAG has potential in the healthcare industry, where access to accurate and timely information is crucial. By retrieving and incorporating relevant medical knowledge from external sources, RAG can provide more accurate and context-aware responses in healthcare applications. Such applications augment the information accessible by a human clinician, who ultimately makes the call and not the model.

• Legal – RAG can be applied powerfully in legal scenarios, such as mergers and acquisitions, where complex legal documents provide context for queries. This can help legal professionals rapidly navigate complex regulatory issues.