What is RAG (Retrieval-Augmented Generation)?
Retrieval-Augmented Generation (RAG) is an AI model that combines information retrieval with generative text generation. It enhances the accuracy and relevance of AI-generated content by integrating external knowledge sources into the response generation process.
How RAG Works
- Retrieval :
The model searches for relevant documents or data from external sources.
- Augmentation :
It incorporates the retrieved information into its response generation process.
- Generation :
The AI generates a more informed and accurate response using the retrieved data.
Applications of RAG
- Conversational AI :
Enhances chatbot responses by retrieving real-time information.
- Content Creation :
Assists in generating fact-based articles and reports.
- Legal and Healthcare Sectors :
Improves accuracy in legal document analysis and medical diagnoses.
RAG models improve the reliability and contextual understanding of AI-generated responses, making them a valuable asset across various industries. AI assistants and chatbot GPT models can utilize RAG for more precise and informative interactions.
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