RAG (Retrieval-Augmented Generation)

RAG stands for Retrieval-Augmented Generation. It is a method in natural language processing (NLP) that combines retrieval-based methods with generative models to improve the quality and accuracy of generated responses.

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How RAG Works

1. Retrieval Phase

   • The system searches a large knowledge base (documents, databases, etc.) to find relevant information based on a user query.
   • Example: A search engine or vector database returns documents related to the question.

2. Augmentation Phase

   • The retrieved information is fed into a generative model (like GPT) as additional context.
   • This helps the model generate more accurate and context-aware responses.

3. Generation Phase

   • The generative model produces the final output using both the original query and the retrieved knowledge.

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Advantages of RAG

• Handles long-tail queries that models might not know offhand.
• Reduces hallucinations in generative AI.
• Can be updated by simply adding new documents to the knowledge base, without retraining the model.

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Example Use Case

Imagine a chatbot for a company:

• User asks: "What is the refund policy for online orders?"
• The RAG system retrieves the latest company policy from the internal database.
• The generative model then crafts a natural, accurate response using the retrieved data.