🤖 RAG: How AI Can Answer Questions Using Your Own Data

Large Language Models are powerful, but they don't automatically know everything inside your private documents, databases, or company knowledge base.

That's where Retrieval-Augmented Generation (RAG) comes in.
🔹 1. User asks a question
The system receives the user's query.
🔹 2. Relevant information is retrieved
The query is converted into an embedding and compared against stored documents in a vector database.
🔹 3. Context is added
The most relevant information is provided to the language model as context.
🔹 4. The AI generates an answer
The model uses the retrieved information to produce a more relevant response.

A simple RAG pipeline looks like:
Documents → Chunking → Embeddings → Vector Database → Retrieval → LLM → Answer


#RAG