RAG - The new Buzzword in LLM

What is RAG?

Retrieval Augumented Generation. It is a method for integrating external information into the language generation process. Essentially, a RAG system will first retrieve relevant documents or information from a large corpus of text and then use this information to inform the generation of its responses. This approach helps to enhance the model's ability to provide accurate and informed answers by augmenting its pre-trained knowledge with up-to-date or more detailed information from external sources. This is specifically useful in enterpris which I wants to use LLMs to improve their responses. A human like conversational interface is possible using RAG systems.

A simple query works like below

In practice, this involves two main components:

1. Retriever: This part of the system is responsible for finding relevant documents or passages given a query. It typically uses a form of similarity search to sift through a large dataset to find the most relevant pieces of information.
2. Generator: Once the relevant information has been retrieved, the generator uses it to construct a response. This component is a language model that can take into account both the initial query and the retrieved documents to generate a coherent and contextually relevant answer.

RAG can be particularly useful for tasks where the language model needs access to specific factual information or when the query is about recent events that the model wouldn't know about from its training data alone. It combines the benefits of neural language models with the vast information available in external textual data. Many of the Models do not know about enterprise specific knowledge base and we need more factial answers from enterprise knowlege base.

A real-world example of using Retrieval-Augmented Generation (RAG) would be in a customer service chatbot for a large company that sells a wide range of products. Such a chatbot needs to provide accurate, up-to-date information on products, handle returns, track orders, and resolve customer issues.

Here's how a RAG system could enhance the chatbot's effectiveness:

1. Product Information: When a customer asks a specific question about a product feature that isn't part of the chatbot's pre-trained knowledge, the retriever can pull the latest product specifications or manuals from the company’s database to provide accurate details.
2. Order Tracking: A customer might inquire about the status of their order. The RAG system can retrieve the customer’s order details from the shipping partner’s API or database and generate a response that includes the current location of the package and estimated delivery time.
3. Handling Returns: If a customer wants to return a product, the RAG system can retrieve the most current return policy and procedures, which might change frequently, and guide the customer through the process step by step.
4. Troubleshooting: For technical support, the chatbot can use RAG to retrieve troubleshooting guides or the latest technical bulletins to help solve a customer's issue with a product.

In each of these cases, the RAG system allows the chatbot to provide responses that are not only contextually relevant but also based on the most current and specific information available, leading to higher customer satisfaction and more efficient service.

To leverage a knowledge base stored in Confluence with a Retrieval-Augmented Generation (RAG) model, you would create a system where a retriever accesses the Confluence API to search and index relevant documents. This indexed information serves as the foundation for the RAG model to draw upon when generating responses. The RAG combines this retrieval mechanism with a powerful language generation model, such as GPT-4, which utilizes the retrieved data to construct accurate and contextually relevant answers. This integration requires ensuring API access, setting up secure and efficient retrieval processes, and potentially fine-tuning the language model with domain-specific data. Once implemented, this system can significantly enhance information retrieval tasks within the company, making it a valuable tool for customer support, internal knowledge sharing, or any application that requires pulling specific information from the company's knowledge base.

Hope you liked the use of RAG which empowers enterprise to take advantage of LLMs like GPT-4, Claude, Llama and at the same time try and use the knowledge base built over time by the enterpise.