The AI hype train isn’t slowing down anytime soon—and two terms that come up frequently in developer circles are Large Language Models (LLMs) and Generative AI. But they aren’t synonymous.
Here’s a developer-centric breakdown of what sets them apart and how they intersect.
What’s an LLM, Exactly?
A Large Language Model is a type of AI that has been trained on massive datasets of textual data. Using deep learning, typically transformer-based architectures, LLMs can generate coherent and context-aware responses. They’re designed for one modality—text.
OpenAI's GPT-4 and Google’s PaLM 2 are prime examples. As developers, we’re using these LLMs to:
- Build chatbots
- Generate code snippets
- Perform semantic search
- Create documentation
They’re the powerhouse behind tools like GitHub Copilot, ChatGPT, and Bard.
Generative AI: More Than Just Text
Generative AI refers to any AI system that can generate new content—whether it’s images (DALL·E), music (MusicLM), video (Sora), or 3D models. LLMs are a subset of this larger category.
It’s like this:
- LLMs = Text-based generative AI
- Generative AI = Everything, including LLMs
Why the Distinction Matters in Dev Projects
When you’re building a product, knowing whether to leverage an LLM vs. a multimodal generative model is key. LLMs are great for knowledge-based tasks and natural language interfaces. But if your app requires AI-generated visuals or speech, you’re entering full generative AI territory.
Top comments (1)
Try explaining to people that AI isn't just chatbots ;)