What Is an LLM?

An LLM (Large Language Model) is an AI model trained on massive amounts of text and other data so it can understand and generate human-like language, answer questions, write code, summarize information, and reason through problems.

Think of it as the brain behind an AI chatbot like ChatGPT, Claude, or Gemini.

1. How does an LLM work?

1. Training data

Books, websites, code, articles, and other data

2. The LLM

Learns patterns, relationships, language, and problem-solving skills during training

3. Your prompt

"Build me a Python application that summarizes PDFs."

4. The output

The model generates an answer, code, or another supported output.

At its core, an LLM generates text by predicting the next token (a word or part of a word) based on the context. Modern models can also develop sophisticated capabilities for reasoning, coding, and following instructions.

2. Popular LLMs

GPT — OpenAI

Powers ChatGPT and other AI applications. Strong across writing, coding, and reasoning.

Claude — Anthropic

Used for coding, analyzing documents, and working with complex instructions.

Gemini — Google

Supports text, coding, images, audio, and other multimodal tasks, depending on the model.

Llama — Meta

A family of models that developers can download in supported releases and run on their own infrastructure.

Qwen — Alibaba

A family of models covering language, coding, vision, and other tasks, with many open-weight releases.

3. What are parameters and billions of parameters?

You may have heard terms such as 7B, 70B, or 405B.

Parameters are the numerical values a model learns during training. They help determine how it processes information and generates responses.

Generally, larger models require more computing resources, but more parameters do not automatically mean a smarter model. Architecture, training quality, and inference techniques also matter.

4. Open-weight vs. closed models

Closed models

Examples: GPT and Claude models offered through their hosted services.

You typically access them through a website or API. The provider manages the underlying model infrastructure.

Open-weight models

Examples: selected Llama and Qwen releases.

You can download the model weights and run them on suitable hardware, subject to the specific model's license and hardware requirements.

5. How does an LLM fit into an AI application?

Imagine you're building your own AI companion or coding assistant.

Your application

Website, mobile app, or voice interface

LLM

Understands requests and generates responses

Memory / RAG

Retrieve relevant information

Tools / APIs

Perform actions and calculations

The LLM is only one part of the system. Your application can combine it with a database, retrieval-augmented generation (RAG), external tools, voice recognition, and text-to-speech.

For example, an AI chef could use an LLM to understand your request, retrieve recipes from a database, and generate a cooking plan based on your ingredients.

One important distinction: An LLM doesn't automatically know your private documents, remember every past conversation, access the internet, or execute code. Those capabilities depend on the surrounding system and the tools connected to it.

The simplest way to remember it

• LLM: The language and reasoning engine.
• AI application: The complete product built around the engine.
• RAG: A way to give the model relevant information from external documents or databases.
• API: A way for your application to communicate with a model hosted by a provider.
• GPU: Hardware commonly used to run and train large AI models.

If you're building your own AI products, the next important concept is understanding the difference between an LLM, an AI agent, and a model like GPT or Qwen—because they aren't the same thing.