Retrieval-Augmented Generation (RAG)
Issue #95 | Feb 7th, 2025
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Hello!!
Welcome to the new edition of Business Analytics Review!
In today’s edition, we are going to discuss Retrieval-Augmented Generation (RAG), an AI framework that enhances generative AI models using external knowledge sources and retrieval-based mechanisms, improving the accuracy, relevance, and informativeness of the generated responses.
RAG combines the strengths of information retrieval systems with large language models (LLMs) to provide more contextually relevant and factually accurate outputs. It is especially useful for maintaining up-to-date information or accessing domain-specific knowledge.
Main components of RAG Models
Retrieval Component: This part fetches relevant information from an external knowledge base, such as a database or collection of documents. The system searches for relevant information based on the input query, which could be a company knowledge base or other structured or unstructured data repository
Generation Component: This component creates a response based on the retrieved information. The LLM uses both the new knowledge and its training data to generate improved responses
Process of RAG
Query Processing: The input query is processed to retrieve relevant information from a knowledge base
Retrieval: A retrieval mechanism searches for passages or documents relevant to the input query
Augmentation: The user input is augmented by adding the relevant retrieved data in context
Generation: The augmented prompt allows the LLM to generate an accurate answer to the user's query
Real-world applications
Customer Service: RAG chatbots provide accurate, prompt answers by retrieving the latest information and understanding query context for relevant, helpful responses
Education: The models personalize learning by generating tailored explanations, questions, and study materials that cater to individual student needs and interests
Finance: It retrieves market data and analyses, ensuring informed investment decisions. It identifies trends by analyzing historical and real-time data
Healthcare: The systems integrate patient data and medical guidelines, offering precise diagnostic and treatment recommendations for healthcare providers
Legal research and analysis: It streamlines legal research by retrieving relevant information, aiding professionals in drafting documents and analyzing cases efficiently
Recommended Video
The video explains Agentic RAG, an enhanced version of RAG, where LLMs intelligently select data sources for better responses. Unlike typical RAG, the LLM acts as an agent, choosing the most relevant vector database based on the query's context. This improves accuracy and allows integration of diverse data, with applications in customer support and legal tech. It also features a failsafe for irrelevant queries.
Trending in Business Analytics
Let’s catch up on some of the latest happenings in the world of Business Analytics:
Google introduces new class of cheap AI models as cost concerns intensify
Google has launched Gemini 2.0 Flash and the new Flash-Lite, a cheaper AI model, amid growing cost concerns and competition from rivals
South Korean ministries block DeepSeek on security concerns
Due to security concerns and unanswered data inquiries, South Korean ministries and police have blocked access to the Chinese AI startup DeepSeek
Russia's Sberbank plans joint AI research with China
Sberbank will collaborate with Chinese researchers on AI projects after DeepSeek's AI model challenged U.S. dominance with a low-cost solution
Tool of the Day: NVIDIA Riva
NVIDIA Riva enhances Retrieval-Augmented Generation (RAG) by adding multilingual speech and translation capabilities to LLM applications. This transforms chatbots into powerful multilingual assistants. Riva enables voice input to RAG systems, facilitates real-time conversational interactions, and helps build low-latency speech-to-speech RAG bots. By integrating Riva's speech and translation microservices, organizations can create RAG solutions that handle live audio, generate dynamic responses, and provide accurate results across various use cases. Learn More
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