What’s the Difference and How Do We Use Them?
If you’ve been following the buzz around artificial intelligence, you’ve probably heard terms like LLMs and Generative AI thrown around. At first, they sound interchangeable, but they’re not exactly the same. In fact, understanding the difference between the two can give you a clearer picture of how AI tools actually work—and how they’re changing the way we create and interact with technology.
Let’s break it down in simple terms.
What is an LLM?
LLM stands for Large Language Model.
Think of it as a supercharged text prediction engine. An LLM has been trained on huge amounts of text—from books, articles, websites, and more. Its job is to understand and generate language based on patterns it has learned.
For example, when you type into ChatGPT, the model predicts what words are likely to come next in a way that makes sense. That’s why it can answer your questions, write essays, or even help debug code.
Key things to know about LLMs:
- They work with text and language.
- They don’t “know” facts in the human sense—they’re making educated guesses based on training data.
- They are the “engine” behind many AI tools you already use.
In short: LLMs are the brains behind text-based AI conversations.
What is Generative AI?
Generative AI is a much broader term. It refers to any AI system that creates new content—not just text, but also images, music, video, or even 3D models.
Examples of generative AI include:
- ChatGPT creating text (powered by an LLM).
- DALL·E or MidJourney generating images from a prompt.
- MusicLM generating new music.
- Runway creating video clips.
So while LLMs are one type of generative AI, they’re not the whole story. Generative AI is the umbrella, and LLMs fit under it.
Think of it like this:
- Generative AI = the entire toolbox.
- LLMs = one of the most powerful tools inside that toolbox.
The Key Differences
Here’s a quick side-by-side comparison:
| LLMs | Generative AI |
|---|---|
| Focuses on language and text | Can create text, images, music, video, etc. |
| Examples: ChatGPT, Claude, LLaMA | Examples: ChatGPT (text), DALL·E (images), Runway (video), MusicLM (music) |
| Acts like a conversation partner, writer, or assistant | Acts like a creator across multiple media |
| Narrower scope but deeper in text | Broader scope across creative formats |
Real-World Example
Let’s say you’re starting a blog.
- With an LLM, you could ask for help writing posts, brainstorming headlines, or improving your grammar. The LLM is handling text-based tasks.
- With Generative AI, you could go beyond text: use a tool like DALL·E to generate a custom image for your blog post, or even create a video explainer to share on social media.
In practice, many people use both without realizing it. ChatGPT, for example, is powered by an LLM, but when OpenAI added the ability to generate images, it became part of the larger generative AI family.
Why This Matters
Understanding the difference helps you:
- Know what tool to use for the job. Want text? Reach for an LLM. Want visuals? Use another generative AI model.
- Cut through the buzzwords. Instead of getting lost in tech hype, you’ll know what’s happening behind the scenes.
- Stay future-ready. As AI tools evolve, companies will mix and match different models (LLMs for text, image models for visuals, etc.) to create even more powerful applications.
Final Thoughts
To keep it simple:
- LLMs are the specialists in generating and understanding text.
- Generative AI is the bigger category that includes LLMs and other models for images, video, and more.
So next time you hear someone talk about “Generative AI,” remember—it’s the big picture. And when you hear “LLM,” that’s the text-focused piece of it.
The two aren’t rivals. They’re teammates. And together, they’re shaping the future of how we work, learn, and create.
CoderMack 
Leave a comment