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RAG

RAG (Retrieval-Augmented Generation) is an AI-powered approach that returns a single AI-generated answer by combining our advanced AI-semantic search capabilities with large language models (LLMs) to provide a direct, contextual answer to the user's question.

Overview

When to Choose RAG?

Choose RAG when you want to provide users with a single AI-generated answer to their query instead of a list of results ranked by relevance.

RAG uses a two-step process:

  1. Smart Retrieval: Finds the most relevant content from your documents using AI-semantic search
  2. AI-Generated Answer: Creates a natural, contextual AI-generated answer based on the retrieved information

Key Features

  • Direct Answers: Generates a single, complete answer instead of returning multiple documents
  • Source-Grounded: Answers are based on your actual content, reducing AI hallucinations
  • Natural Interaction: Users can ask questions in conversational language
  • Conversation Context: Maintains context across multiple questions, enabling follow-up questions and clarifications
  • Real-Time Updates: Answers reflect your latest document content

Best Use Cases

RAG excels in scenarios where:

  • Users need a single, direct answer rather than browsing through a list of results
  • Questions require synthesizing information from multiple sources
  • Users engage in multi-turn conversations with follow-up questions
  • Content is frequently updated and answers need to stay current
  • Natural, conversational interactions are preferred

Benefits of Gainly RAG vs. LLMs

Here's how RAG compares to using a standalone LLM for implementing search:

Enhanced Accuracy and Relevance

  • RAG: Retrieves context-specific information from your documents before generating an answer, ensuring responses are directly relevant to the query
  • LLM: Generates answers based on pre-trained data that might not be up-to-date or relevant to your specific use case

Reduced Hallucinations

  • RAG: Uses retrieved documents to ground its answers, significantly reducing incorrect or nonsensical responses
  • LLM: Can sometimes generate plausible-sounding but incorrect information, referred to as "hallucinations"

Up-to-Date Information

  • RAG: Gainly RAG is built with our AI-Semantic Search, a real-time retrieval system. As a result, it generates answers based on the most current information in your documents
  • LLM: Limited to information from its training data, which becomes outdated over time

Cost-Efficiency

  • RAG: More cost-efficient by retrieving and processing only the most relevant information
  • LLM: Requires significant computational resources to process queries against its entire knowledge base

Transparency and Explainability

  • RAG: Provides transparent source attribution, making it easy to trace the origin of information
  • LLM: Difficult to trace the source of information as it draws from vast amounts of training data

List of Results vs. AI-Generated Answer

If you want the search to return a list of results instead of a single AI-generated answer, we recommend Hybrid Search which combines the best of AI-Semantic and Lexical search techniques.