𝗥𝗔𝗚 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀

𝗥𝗔𝗚 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀

AI models like ChatGPT have a limit. They only know their training data. They do not know your private files or new data. This leads to wrong answers.

RAG fixes this. RAG stands for Retrieval-Augmented Generation.

It works in two steps. First, it finds the right info from your files. Second, it gives this info to the AI. The AI uses your data to give a correct answer.

Example: You ask about your company leave policy. RAG searches your HR PDF. It finds the policy. The AI tells you the exact rule.

The process:

• Extract text from PDFs or websites.
• Split text into small pieces.
• Turn pieces into numbers called vectors.
• Store vectors in a database.
• Find the best piece when you ask a question.
• Give the piece to the AI.

Why RAG is better:

• You do not need to retrain the AI.
• Updates happen fast.
• Answers stay based on facts.

Tools you need:

• Language: Python.
• Frameworks: LangChain or LlamaIndex.
• Database: Pinecone or ChromaDB.
• AI: GPT-4 or Llama.

Build your own AI assistant using your own data.

Source: https://dev.to/pavan_barnana_/rag-retrieval-augmented-generation-explained-for-beginners-build-ai-applications-using-your-own-1g50 Optional learning community: https://t.me/GyaanSetuAi