People often say things like I used an LLM to generate an image, I used an LLM to clone my voice or I used an LLM to make a video. In most cases, this is not technically correct. What they actually used was Generative AI. The confusion comes from the fact that most users interact with AI through chat style tools, so it feels like one single model is doing everything. In reality, different models play very different roles.
Generative AI is the broad field that includes any system that can create new content. That includes text, images, audio, video, code and even scientific structures. Image generators, voice synthesis tools, video creators and music generators all fall under Generative AI. These models are trained on very different types of data such as pixels, sound waves or motion patterns.
An LLM, or large language model, is just one type of Generative AI. It is trained only on text. Its job is to understand language, generate language, reason, summarise and follow written instructions. It does not generate images, sound or video by itself. It only works with words.
In tools like ChatGPT, the LLM acts as the language interface. It reads your prompt, understands your intent and decides what should happen next. If you ask for an explanation, it responds directly. If you ask for an image or audio, it sends structured instructions to a specialised Generative AI model that actually creates the content. The LLM then explains the result back to you in plain language.
This is why saying I used an LLM to generate an image is usually giving credit to the wrong part of the system. The LLM helped translate your idea into instructions. The Generative AI model did the actual creative work.
Understanding this difference leads to better expectations, better tool choices and smarter AI system design. LLMs are powerful, but they are not all purpose generators. They are the language brain sitting on top of a much larger Generative AI ecosystem.
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LLMs and Generative AI are not the same!
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