Retrieval-Augmented Generation (RAG) Introduction

 Define

Retrieval-Augmented Generation (RAG): This is a solution that provides large language models (LLMs) with additional information from external knowledge sources. This allows them to generate more accurate and contextually relevant responses, while reducing hallucinations.

Problem

Modern LLMs are trained on large amounts of data to achieve a broad spectrum of knowledge stored in the weights of the neural network (parameter memory). However, requiring an LLM to generate a response that requires knowledge not present in its training data, such as new information, proprietary information, or specialized information, can lead to practical inaccuracies (illusions).

Solution

Combining a generative model with a retrieval module to provide supplementary information from an external knowledge source can be more easily updated. Simply put, RAG for LLMs is like an open-book exam for humans. In an open-book exam, students are allowed to bring reference materials, such as textbooks or notes, which they can use to look up relevant information to answer questions. The idea behind an open-book exam is to test students' reasoning skills rather than their ability to memorize specific information.

Use cases

1. Customer support: Improve chatbot responsiveness to updated company policies and product details.

2. Support for research: Providing researchers with the latest studies and articles related to their query.

3. Medical: To provide healthcare professionals with the latest medical guidance and research.

4. Education: To support students with accurate and up-to-date information for their studies.

5. Finance: Helps financial analysts access the latest market data and reports.

RAG (Retrieval Augmented Generation) Workflow

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