In a recent article we discussed how Retrieval Augmented Generation (RAG) works and how it can be beneficial for knowledge workers. In this article we provide a list of use-cases across various industries where RAGs are specifically interesting. If you are interested in use-cases specifically for finance, please check this article: 5 RAG Use Cases in Finance.
Document Search and Summarization:
Let’s say you’re a lawyer or researcher needing to sift through hundreds of documents to find key details. A RAG system can search through all the relevant texts and generate a concise summary of the most important points, saving you hours of time and effort.
Answering Complex Questions:
In applications like chatbots, virtual assistants, or online tutoring, RAG models can greatly enhance the quality of answers. Since they can pull the latest info from a database or the web, they’re especially good for answering factual questions that require specific, up-to-date details.
Personalized Recommendations:
If you’ve ever used a recommendation engine for shopping or content discovery, imagine that with a RAG model behind it. It can pull in specific information based on your past behavior or preferences and generate personalized suggestions—whether that’s a product recommendation or tailored content for you to read or watch.
Research Help:
For academics or scientists, RAG can be a game-changer. Instead of manually digging through countless research papers, the model can pull the most relevant studies and even generate a summary of key findings, helping researchers stay on top of their field without the need for exhaustive searches.
Content Creation and Management:
For companies that manage large amounts of content, a RAG system can help generate new material that fits seamlessly with existing resources. Whether it's writing blog posts, newsletters, or internal reports, the system can pull relevant data from the archives and weave it into fresh, cohesive content.
Customer Support:
Imagine you’re chatting with a company’s support bot, and it needs to give you a solution to your problem. Instead of relying on outdated or limited info, a RAG model can quickly search through the company’s knowledge base or FAQ and come back with the most recent and relevant solution—tailored to your specific issue.
Healthcare Assistance:
In healthcare, having the latest information is critical. A RAG system can assist doctors and patients by pulling in the most recent medical guidelines, research, or case studies and integrating that data into helpful, personalized advice—whether for treatment plans or patient education.
News Summarization:
Think of a news aggregator powered by RAG. It could pull in stories from various sources and provide a concise yet comprehensive summary, giving readers a quick but well-rounded understanding of the day’s events without missing any key details.
Want to learn more?
Are you interested in how your organisation can benefit from Retrieval Augmented Generation (RAG) to give superpowers to your knowledge workers? Book here a demo to learn more about Structize AI!
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