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Generative AIvsLLM
What is the difference between generative AI and an LLM?
Updated 2 min read7 differences
In short
Generative AI is any AI that creates new content, such as text, images, audio or video. An LLM is one kind of generative AI, specialized in text and code.
Generative AI
Generative AI is artificial intelligence that creates new content, such as text, images, code, or audio, based on patterns learned from existing data.
Read the page on Generative AILLM
Large Language Model
An LLM is a machine learning model trained on huge amounts of text that generates language by repeatedly predicting the next most likely piece of text.
Read the page on LLMGenerative AI and LLM compared
| Aspect | Generative AI | LLM |
|---|---|---|
| Scope | A broad category of AI | One kind of generative AI |
| Creates | Text, images, audio, video and code | Text and code, plus other media in multimodal models |
| Typical architectures | Transformers, diffusion models and others | Transformers |
| Trained on | Data in the medium it generates: text, images, sound | Huge collections of text and source code |
| Input and output | Depends on the model: many media in, many media out | Text in and text out, often with images as input too |
| Examples | LLMs, image generators, voice and music models | The models behind chat and coding assistants |
| Main risks | Hallucinations, deepfakes, copyright questions | Hallucinations, prompt injection, outdated knowledge |
The difference, explained
Generative AI is a broad category: models that produce new content instead of only classifying or scoring existing data. It includes models that write text, draw images, compose music, synthesize voices and generate video. A large language model (LLM) is one family within it: a neural network, usually a transformer, trained on huge amounts of text to generate language by predicting one token after another.
The relationship is that of a category and a member, like vehicles and cars. Every LLM is generative AI, but not all generative AI is an LLM: image generators are usually diffusion models, which start from noise and refine it into a picture, and audio and video models use their own architectures. LLMs became the best-known kind because text and code cover so much work, from chat assistants to coding tools.
The lines are blurring. Many modern models are multimodal: they accept images, audio or documents as input, and some produce images or speech as well, so a model that started as an LLM can grow into a general generative system. The name LLM is still used for these models, because language remains at their core.
A common misconception is that generative AI simply means chatbots. A chatbot is an application built on an LLM, while generative AI also powers image editing, music, synthetic data and design tools. Another is that generative AI is the whole of AI: classic machine learning for prediction and classification doesn't generate anything and remains widely used.
Which one should you use?
Choose Generative AI when…
- You mean the whole field, including image, audio and video generation.
- A product combines several kinds of generated media.
- You discuss policy, risks or strategy that apply to every content-generating model.
Choose LLM when…
- You mean a model that reads and writes text or code.
- You work with prompts, tokens, context windows or tool calling.
- You compare specific language models by size, cost or context length.
Readers ask
Is ChatGPT generative AI or an LLM?
Both, in a sense. ChatGPT is a chat application built on large language models, and those models are a kind of generative AI.
Are image generators LLMs?
No. Most image generators are diffusion models, which turn random noise into an image step by step. They are generative AI but not language models, although they rely on a text encoder to understand the prompt.
Is every AI model generative?
No. Many models classify, rank or predict, such as spam filters, fraud detection or price forecasts. These discriminative models analyze existing data instead of creating new content.