Gen-AI: Retrieval Augmented Generation

Gen-AI is a Generative AI framework used in natural language processing. It combines two techniques:

1. Retrieval-based method: Retrieving relevant information from a large collection of documents or knowledge resources.
2. Generation-based method: Generating a response based on the retrieved information and an input.

By combining these techniques, Gen-AI generates text that is more accurate, relevant, and informative.

Application of RAG

RAG is useful for tasks requiring a combination of factual awareness and generative capabilities, such as:

• Conversational agents
• Question answering systems

How it works

• Retrieval-based models are trained using huge datasets to retrieve relevant data when presented with a query.
• Generative models can generate new text content (e.g., LLM models).

Example:

Query: "Capital of France"

• Retrieval: "Paris is the capital of France"
• Generation: "Paris"

The ability to retrieve relevant information enhances the accuracy of generation.

RAG is an emerging technology in Generative AI. For example, Google search uses RAG techniques.