𝗪𝗵𝗮𝘁 𝗜𝘀 𝗥𝗔𝗚 𝗶𝗻 𝗔𝗜?

𝗪𝗵𝗮𝘁 𝗜𝘀 𝗥𝗔𝗚 𝗶𝗻 𝗔𝗜?

AI tools often struggle with unstructured documents. You need accurate answers from your files.

Retrieval-Augmented Generation (RAG) solves this.

RAG mixes two processes. First, it finds the right data. Second, it writes the response.

Why you need RAG:

• It reads unstructured documents.
• It helps customer support teams.
• It works for healthcare data.
• It speeds up research.

How RAG works:

• Data Prep: Clean your text.
• Retrieval: Find the right document.
• Generation: Create the response.
• Result: Get your answer.

You use Python libraries for this. The transformers library is a top choice. You use a tokenizer to process text. A retriever finds the facts. The model writes the final answer.

RAG makes AI responses accurate. You get the right info fast.

Source: https://dev.to/pulsetechhub/yapay-zeka-ile-belge-okutma-rag-nedir-ve-neden-onemlidir-2207 Optional learning community: https://t.me/GyaanSetuAi