Large language models (LLMs) have significantly transformed the way we interact with information. However, LLMs have some limitations, one of which is that their training data, while enormous, may not include data that you want them to use while answering questions. For example, suppose that you run a financial consultancy business and have internal, private data that is not published anywhere on the internet. This means that the LLMs did not have access to that data while training, and so cannot answer any questions related to it. This is where retrieval augmented generation (RAG) systems come in which allow you to seamlessly connect LLMs with your own knowledge databases.
In this article, we will explore in more detail what RAGs are and go through some real-world examples.
What is Retrieval Augmented Generation?
LLMs without RAG augmentation. In this case the LLM only has access to publicly available information and cannot answer any questions that rely on internal and private knowledge bases.
Retrieval augmented generation (RAG) systems combine the generative powers of LLMs with information retrieval abilities typical of search engines. By doing so, RAG enables models to access a wealth of external information during the generation process, leading to more informed and contextually accurate outputs. This method is in contrast to the standard approach where LLMs generate responses based solely on their pre-trained data.
There are generally two main components of a RAG system:
1. Retrieval: LLMs usually have a limit on the length of an input prompt. You may have used ChatGPT and encountered an error saying that the prompt is too long and needs to be shortened. This means that if you have a large knowledge base of documents, you cannot directly feed all of it to the LLM. So, in the first step, RAG systems take the user's prompt and selects relevant information from the external knowledge base that may contain information relevant to answering that query. This is done through semantic searching, similar to what most search engines do. The extracted relevant information is often called the context.
2. Generation: Next RAG uses the context and the user's query and feeds that to an LLM and asks it to generate an answer to the user's query by using the information in the context.
LLMs augmented with retrieval capabilities allow them to answer questions based on information other than what they were trained on.
Use Cases of RAG Systems
An application of RAG is in the customer service industry. For instance, a telecommunications company might use a RAG-enabled model to handle customer queries. When a customer asks a specific question about their billing plan, the RAG model can retrieve the most recent and relevant billing information and policies from the company's knowledge base to provide a precise answer. This not only improves the customer experience but also reduces the workload on human agents.
Another significant application of RAG is in research and development sectors, especially in fields like pharmaceuticals and biotechnology. Researchers can use RAG-integrated models to pull data from scientific papers and internal research documents and answer questions. This will allow researchers to simply query the RAG system with any questions mitigating the need to go through dozens of documents to find the relevant information.
Challenges in Implementing RAG:
While the benefits are substantial, there are several challenges that organizations may face when implementing RAG:
1. Integration complexity: Combining LLMs with a dynamic knowledge base requires sophisticated programming and system design, especially for large-scale knowledge bases.
2. Efficient retrieval: RAG's effectiveness heavily depends on how well it is able to pull relevant information from the knowledge base that can help answer the user's query.
3. Latency Issues: Retrieval from big knowledge bases can introduce delays, increasing the response time of the model, and affecting user experience.
