LLM = Neural Network Model?

Now we can return on LLM and Neural Network Model, do they mean the same thing? No!

LLM is a subset of NNM (Neural Network Model).

• NNM (Neural network model) – any trained neural network, no matter what it’s for: images, sound, text, games, etc.
• LLM (Large Language Model) – a specific type of neural network model that’s trained on huge amounts of text to process and generate human-like language.

So let’s recap here…to make this sink:

• If a Neural Network Model (NNM) is trained to predict the next word in text – it’s called a Large Language Model (LLM).
• If an NNM is trained to predict the next pixel or image – it’s called an image generation model (like DALL·E, Midjourney, Stable Diffusion).
• If an NNM is trained to predict the next sound sample – it’s an audio model (like text-to-speech models or music generators).
• If an NNM is trained to predict moves in a game – it’s a game-playing model (like AlphaZero for chess/Go).

ChatGPT = LLM?

Now let’s ask, is ChatGPT the same as LLM? No.

ChatGPT is the product built using LLM. In the ChatGPT, the LLM part is called GPT-4 or GPT-5 etc. But ChatGPT itself consists of a number of other stuff, like the code that handles the conversation, safety filters, formatting tools, memory/session management, the chat interface (the website, api, the mobile app).

GPT-4 / 5 – is like the brain of it all, the LLM. The core. ChatGPT is like the brain plus the body, eyes, ears, and personality wrapper that lets you talk to it easily.

So if I were to divide the process into layers, kinda similar to our famous TCP/IP OSI 7 layers, I think the AI LLM analogy may look like this:

[User Question] → Interface Layer (UI) → Client Processing (Request Packaging) → API & Gateway (Routing & Auth) → Preprocessing (Tokenization + Instructions) → Core Model (LLM) → Postprocessing (Detokenization + Filtering) → Output to UI → [Answer Shown]

With analogy with the TCP/IP OSI Model, lets name this new model – Prompt Flow Model.

Prompt Flow Model

Layer 1 – Interface Layer (Application UI)

The AI chat website or app you see. This is where you type text, press “Send,” and view responses.

Layer 2 – Client Processing Layer

Code in the browser/app that takes your typed text, packages it into a request. Sends your input to OpenAI’s servers via an API call. Like an email client sending your message to the mail server.

Layer 3 – API & Gateway Layer

OpenAI’s API endpoint that receives your request. Checks authentication, routes your message to the correct model (e.g., GPT-4), applies usage limits.

Layer 4 – Preprocessing Layer

System that prepares your text for the model. Scans for obvious harmful content, prompt injection attempts, banned topics. Cleans/normalizes text, tokenizes it (breaks into word pieces and smallest units of meaning), and adds any system instructions (e.g., “be polite” or “don’t output unsafe content”). Translating your message into a language the LLM / “brain” understands.

Layer 5 – Core Model Layer (LLM = GPT-4 or GPT-5)

The trained neural network model. Takes your tokens, processes them through billions of parameters, and predicts the most likely next tokens step-by-step until a full response is formed. This is the brain / LLM thinking through your question.

Layer 6 – Postprocessing Layer

Code that takes the raw tokens from the LLM and turns them into readable text. Converts tokens back to words, applies formatting, maybe runs them through content filters. Scans the generated text for harmful content that slipped through. This is the step of turning a brain’s thoughts into coherent speech.

Layer 7 – Output Layer

The final message sent back to your app/browser. Displays the generated answer in your chat window.

By: Vahe Demirkhanyan, Sr. Security Analyst, Illumant