LLM, RAG & AI Agents in Simple terms

LLM, RAG & AI Agents in Simple terms

1. LLM (Large Language Model) = The Super Smart Chef

Think of the LLM (like ChatGPT) as a super smart chef in the kitchen.

  • You give it a request like: “I want something spicy, vegetarian, and filling.”
  • The chef (LLM) uses all its knowledge to whip up a dish on the spot, based on recipes it learned before (from cookbooks, blogs, etc.).
  • But the chef doesn’t know today’s special ingredients or what’s in the fridge right now.

So the dish might be good, but it could be outdated or not perfectly relevant.


📚 2. RAG (Retrieval-Augmented Generation) = Chef + Smart Waiter with Today’s Menu

Now imagine there’s a smart waiter who helps the chef.

  • When you place your order, the waiter first checks today’s menu, recent customer reviews, or the restaurant’s latest stock.
  • Then the waiter brings that info to the chef, saying: “Here’s what we have fresh today and what people are liking.”
  • Now the chef makes your dish using both his general knowledge and up-to-date info.

This is RAG — the combination of:

  • Retrieval (the waiter getting relevant info),
  • and Generation (the chef creating the final response).


🤖 3. AI Agents = The Full-Service Staff That Takes Action for You

Now imagine you don’t even walk into the restaurant.

Instead, you have a virtual assistant (AI Agent) who:

  • Takes your order (e.g., “I want to eat a healthy meal tonight”),
  • Checks which restaurants are open nearby,
  • Books a table,
  • Orders the food,
  • And even schedules a cab to get you there.

These AI Agents don’t just generate text like the chef — they:

  • Plan,
  • Fetch info,
  • Take actions on your behalf.

They work like a personal butler or concierge who can use LLMs and tools like RAG to get things done for you.


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Bilkis Jahan Eva

sales representative @AgentGrow

1mo

I love the restaurant analogy—it's such a straightforward way to break down complex ideas. How do you decide which analogies stick best when explaining tech concepts to non-tech audiences? Do you have a go-to method?

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