Leveraging Large Language Models (LLMs) in our work has never been easier. These are incredibly powerful and genuinely open up a whole world of enhancements we can make to the applications that we’re building. While these models are fantastic at communicating to us like humans in natural language, they are typically a bit behind in their knowledge of the world. So, let’s say I’m working on a chatbot that allows users to ask questions about my company's services. I’ll get answers that seem to be from another person, but will they be factually relevant or correct? Probably not. At best, the answers will be dated, and more than likely, the model won’t have any knowledge of my services at all. This common problem can be solved by leveraging RAG.
Retrieval Augmented Generation (RAG) is a powerful technique that can significantly enhance the performance of AI systems that rely on generating accurate and informative responses. By integrating an information retrieval component, RAG allows your application to leverage external knowledge, resulting in more reliable and contextually appropriate outputs. Whether you're building chatbots, virtual assistants, content generation systems, or even search engines, considering RAG as part of your architecture can take your application to the next level.
In chatbots and virtual assistants, RAG enables more engaging and informative conversations by allowing the AI to draw upon relevant external knowledge to provide accurate and specific responses. Similarly, RAG can help produce more coherent and contextually appropriate content in content generation systems by incorporating relevant information from reliable sources.
RAG can also enhance search systems by leveraging the power of language models to grasp the semantic meaning and context of the user's query, going beyond simple keyword matching. This is already a nice win, but we can take it further. With RAG, we can layer on semantic understanding with relevant knowledge retrieved from an external source. This search system provides results that are more closely aligned with the user's needs and context. This contextual relevance is a key selling point for RAG-enhanced search systems, as it significantly improves the user experience and satisfaction by delivering more targeted and relevant information.
Here's how RAG can benefit AI systems
Improved Accuracy
RAG delivers more accurate responses by leveraging external knowledge to supplement the AI's understanding, reducing the likelihood of generating incorrect or irrelevant information.
Contextual Relevance
By incorporating relevant information from external sources, RAG enables AI systems to provide more contextually appropriate responses tailored to the user's specific needs and intent.
Enhanced User Experience
With more accurate, informative, and relevant outputs, RAG can significantly improve the user experience across various AI applications, increasing user satisfaction and engagement.
Technical Approach
At a technical level, RAG introduces an information retrieval mechanism that fetches relevant data from an external knowledge source to augment a pre-trained large language model (LLM). We typically build an automated system that does this work for us. It can take many different shapes depending on the data and its source, but the idea is that we want this additional information as up-to-date as possible.
The LLM itself remains unchanged, and there's no need for additional training. Instead, RAG provides a way to incorporate up-to-date and domain-specific information at inference time dynamically. The retrieved information is then incorporated into the input prompt fed into that LLM, allowing the model to attend to both the original query and the retrieved augmentation to generate the final output text.
