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What is RAG approach to get answers from private knowledge or data by using LLM

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What is RAG?

RAG stands for Retrieval Augumented Generation, is a technique to get answers for precreated knowledge corpus of the data.

What are steps executing in RAG

In RAG approach user can provide query ( question ) in natural language format, then as a second step, we need to find relevant text/data/document from data base, and as third step we need to prepare new prompt by combining user query with relevant document text, we need to send this new prompt to LLM to get appropriate answer

Steps in RAG approach

The user enters a prompt

Retrieve the best document that is relevant to the prompt

Create a new prompt that includes the user’s question as well as the context from the document

Give the newly crafted prompt to the language model

Return the answer to the user



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