What is RAG?

Retrieval augmented generation (RAG) is a method where a system analyzes your query (prompt), finds (retrieves) relevant data from a targeted dataset, and feeds it to an LLM (like ChatGPT or Grok) to generate more accurate, contextually relevant, and maybe real time response.

Simple example:

Imagine you have an error in your code. you copy the error message, find the part of the code that likely caused it, and paste both into ChatGpt. Then you ask it to fix the issue.

That's basic RAG, you're doing it manually! You're retrieving relevant context (the code) and giving it to the AI so it can generate good answer.

Now imagine you don't have to find the code yourself, tools like cursor or other coding agents do that part automatically. They search your codebase, grab the most relevant code related to the error, and feed it to the AI.

Build your own RAG pipeline, make your LLM smarter, and automate your tasks!