When you start to learn about AI, you might think about the risks involved before implementing it. How much of an impact would a certain percentage of errors have on your work or business? You might start to question if implementing AI is even the right choice, constantly going over these issues in your head.
Retrieval Augmented Generation (RAG) is a search-enhanced service that retrieves necessary information from a vast database.
When you use RAG, it tends to have fewer hallucinations compared to the widely used ChatGPT. Plus, you don’t need to run AI with large parameters; you get the desired results even with smaller ones. It’s like taking an open-book exam. The amount of training our AI chatbot services receive is crucial. But with RAG, you can get AI engines with smaller parameters to generate answers using the open-book information we provide.
For example, in customer support chatbots, if you configure RAG to prioritize your company’s product data, you can get accurate answers. Many customer support chatbots are already in use, and if you can update information in real-time as a source for the open-book(RAG), you can provide quick and personalized responses.
Another example is using it for onboarding new employees. They could adapt quickly. Previously, they had to go through mountains of work manuals and ask their seniors. Now, new hires can use RAG AI to ask questions and get the relevant information. This reduces the workload of HR and senior staff and allows new hires to start working immediately, essentially saving money for the company.
For me, content creation is all about trends. I need to understand the importance of timeliness to capture the attention of your audience. But it’s not an easy task. I always need at least an hour or two every day to keep up with trends and maintain insights. I believe RAG AI can help my content marketing through content feedback. If my customer and trend data can provide feedback on my writing, I can create content with a higher success rate. Think about the impact even a few percentage points can have.
RAG is currently being developed in many fields. It’s a specialized field. It’s highly useful for legal, medical, and financial institutions, where you can input vast amounts of information specific to each institution and have the AI use that information to generate responses. This can be applied internally or as an external service. Of course, it’s revolutionary for education, allowing for personalized learning for each individual.