27/08/2026
RAG isn't as simple as it sounds. ๐ง
Most people assume AI models "search the internet live" every time you ask a question.
Reality? They only answer from their learned knowledge โ unless you feed them external context.
That's exactly what RAG (Retrieval-Augmented Generation) solves.
Here's how it works, step by step:
โ You ask a question
โ It gets converted into a vector
โ Relevant data is retrieved from a database
โ The LLM generates an answer using that retrieved context
This is why RAG-based systems are more accurate, up-to-date, and far less prone to hallucination โ because the model isn't guessing, it's checking before answering.
Full breakdown in the infographic below ๐
๐ If you're working with LLMs or building AI systems, save/repost this โ it's a handy reference for later.
๐ฌ Have you implemented a RAG pipeline yourself, or still exploring the concept? Let's discuss in the comments.
