RAG and its Applications in Finance
Retrieval-Augmented Generation (RAG) is one of the most successful applications of AI and Large Language Models (LLMs). A RAG system allows a language model to generate text and answer questions based on a specific context. The context is typically a database of information that is relevant to the questions at hand. Companies in all industries are using RAG to power their search and conversational AI use cases. One of the most common use case has been to build AI chatbots for customer support. We can build a RAG system for our customers to answer their queries about our products. We can use RAG to power our search functionality and recomendation system. Given the power of RAG, it is important for finance professionals to understand how RAG works and its applications in the finance industry.
How RAG works?
RAG stands for Retrieval-Augmented Generation. Let's try to understand what it means. Let's look at how ChatGPT works. When you ask ChatGPT a question, it generates an answer. The keyword here is 'generate'. ChatGPT is a large language model that is trained on a massive amount of text data. It uses this training to generate an answer to your question based on the patters it has learned during training. Since it's generating the answers, it is prone to hallucinations (giving you an answer that is not correct). What if you wanted to ask ChatGPT a question about your company? It might not know the answer to that question because it was not trained on your company's data. Sometimes it may know the answer but it may not be based on the most recent information. This is where RAG comes into picture.
RAG allows us create custom chatbots where the chatbot is provided with specific context and then it answers your questions based on that context. For example, let's say you work in a bank and you have a 100 different products in your bank. You want to create a chatbot that can answers questions specifically about these products. This a perfect use case for building a RAG system. There are two parts to how RAG will work: Retrieval and Generation.
In a RAG system, we first find relevant information from a database of information that is relevant to the questions at hand. In our example, this is the database of information about the bank's products. This is called retrieval. Once we have the relevant information, we pass it to the language model to generate an answer. The language model uses this information to generate an answer to your question. Since the language model has access to the most recent information, it is less prone to hallucinations, and is able to provide you with more accurate and relevant answers.
