What is RAG?

One of the key limitations of Large Language Models (LLMs) is that their training data is fixed as of a certain date, meaning they lack access to the most recent information. Additionally, LLMs are typically trained on broad, general-purpose data and are not tailored to specific domains or use cases unless explicitly fine-tuned. A Large Language Model (LLM) generates responses based solely on the data it was trained on—essentially, its static knowledge.

This is where RAG becomes valuable — bridging the gap by enabling real-time access to relevant and up-to-date information!

What is RAG?

RAG stands for Retrieval-Augmented Generation. As the name suggests it merges two important aspects

1. Retrieval: It retrieves relevant information from a large database or knowledge source
2. Generation: It is use to generate contextually accurate, relevant responses

By integrating these capabilities, RAG systems can both access precise information and generate human-like outputs, making them highly effective for a wide range of use cases.

Use Cases

1.  Chatbot: Company XYZ aims to develop a chatbot for its products and can leverage RAG to ingest and index product-specific documents. When an end user submits a query, the chatbot utilizes the LLM alongside the retrieved documents to deliver accurate and relevant responses.
2. Legal assistants: A law firm can leverage RAG for drafting a legal opinion by citing relevant sections from acts and past judgments. 
3. Financial Advisory Chatbots: An interactive tools for investment advice and portfolio guidance can leverage RAG for retrieving real-time market data and company filings to generate personalized advice
4. Fraud Detection Support: Retrieves relevant case precedents or internal SOPs and contextualizes the behavior to explains suspicious transaction patterns based on internal policies

How it Works?

1. Ingest External Data Source
   1. External data encompasses information not part of the LLM’s original training set.
   2. It may originate from APIs, databases, file systems, or document repositories and can be either structured (like tables) or unstructured (like long-form text).
      
2. Add Context
   1.  When system receives query from user it searches the ingested data
   2.  It finds out the relevant information
3. Enhance the Prompt
   1. The retrieved information is then used to enrich the original user prompt.
   2. This is achieved through prompt engineering, which formats the combined input for optimal understanding by the LLM.