When people first start working with Large Language Models (LLMs), it is easy to think that an AI application is simply: User → LLM → Answer.
For a basic chatbot, this may be enough. But most real-world AI applications have more pieces working together. A common architecture looks like this: Application + LLM + Prompt + Tools + RAG + Function Calling
Let’s start with the big picture.
The Application
The application is what the user interacts with. It could be a website, mobile app, chatbot, or an internal business system. The application receives the user's request, prepares the information for the LLM, and handles the result. Think of it as the layer that connects everything together.
The LLM
The LLM is the main language and reasoning engine. It can understand questions, follow instructions, analyze information, and generate answers. But an LLM has an important limitation: It does not automatically know your private data or the latest information.
The Prompt
A prompt is the information and instructions we send to the LLM. A good prompt helps the model understand what to do and how to respond. But even the best prompt has limits. If the model does not have the information it needs, better wording will not magically create that information. That is where Tools and RAG come in.
The Big Picture
A modern LLM application can be simplified like this:
Application
|
Prompt
↓
LLM
/ \
/ \
Tools RAG
| |
APIs / DBs Knowledge
The LLM is not the whole application. It is one part of the system. The prompt tells the LLM what to do. RAG gives it useful knowledge. Tools allow it to interact with other systems. And the application connects everything together. In the next post, we’ll look at Tools and Function Calling, and see how an LLM can move from simply answering questions to interacting with real-world systems.
