Large Language Models (LLMs) like GPT process input text and generate output in a single step, based on the data it was trained on. For example, when a user queries or “prompts” an LLM with the question “What is the Capital of India?”, the LLM, using its pre-trained knowledge, produces an answer “Delhi”. However, while Generative AI and LLMs are revolutionary technologies that solve many business problems, there are a few limitations that do not fulfill the requirements of all business scenarios. Following are a few examples:
Retrieval-Augmented Generation (RAG) addresses these limitations by accessing reliable external data sources like documents, databases, applications, or the Internet. The data accessed by RAG is current which enhances the accuracy of the responses. Additionally, integrating your proprietary data provides additional context, which means that the LLM is less likely to hallucinate or provide incorrect or irrelevant responses. However, it is important to note, that while RAG can access the latest data, the accuracy and reliability of its responses depend on the quality and recency of the external data sources.
Following is a high-level architecture of a RAG-powered LLM.
Figure 1: High-Level architecture of RAG
How RAG Works?
To better understand how RAG functions, let's consider a specific example.
For instance, a person named Mark joins his new organization, “X Company”. He wants to understand the company’s policy on remote work. He uses his company’s RAG-enabled LLM system to find out the required information by querying or “prompting” it. The key steps involved in the generation of a response include:
This is just one example that highlights the capabilities of a RAG-enabled system. However, organizations must understand, that while LLMs are versatile and adaptable for a broad range of general-purpose language tasks, RAG can be used for business case requirements as well. As with most applications of AI technology, businesses can initially focus on use cases that involve tedious, manual, and time-consuming tasks. Some of the potential use cases for RAG Systems include:
From the above examples, it is clear that RAG-based systems have diverse capabilities. By using the latest data available, RAG systems overcome traditional shortcomings by providing accurate, up-to-date, and contextual responses. The use cases are diverse - from enhancing customer experience by providing immediate and precise information to supporting financial teams with economic insights for report generation. The benefits of RAG solutions are substantial, saving cost, time, and effort. As this technology continues to evolve, its potential will expand further, unlocking even greater advantages and transforming various industries in profound ways.