AI, ML, GenAI and LLMs are NOT the same thing.

tgroenwals shared this post · 5h ago

AI, ML, GenAI and LLMs are NOT the same thing.
Here's the difference, in plain English:

So here's the simplest way to think about it...
They're not competing tools.
They're a hierarchy that keeps building on itself:
(See the visual for the full breakdown 👇)

1️⃣ AI - Artificial Intelligence
↳ The broad umbrella. Systems that can think, reason, and act intelligently.
↳ Examples: expert systems, robotics, game AI.

2️⃣ ML - Machine Learning
↳ A subset of AI that learns patterns from data.
↳ Examples: spam detection, recommendations, fraud detection.

3️⃣ DL - Deep Learning
↳ A subset of ML using neural networks with many layers.
↳ Examples: image recognition, speech recognition, self-driving cars.

4️⃣ GenAI - Generative AI
↳ A subset of DL that creates new content: text, images, audio, video, code.
↳ Examples: ChatGPT, Midjourney, Claude, Stable Diffusion.

5️⃣ LLMs - Large Language Models
↳ A type of GenAI focused on understanding and generating language.
↳ Examples: GPT-4.5, Claude, Gemini, Llama, Mistral.

6️⃣ RAG - Retrieval-Augmented Generation
↳ GenAI combined with external knowledge for accurate, up-to-date answers.
↳ Examples: enterprise chatbots, company copilots, document Q&A.

7️⃣ Agentic AI - Autonomous AI agents
↳ Systems that reason, plan, use tools, and take actions toward goals.
↳ Examples: Claude computer use, Coding Agents.

The simple flow looks like this:
Machines that think → learn → learn deeper → create → understand language → use knowledge → act on their own.

Every layer sits inside the one above it.

You can't choose the right approach for a problem you can't name.
And you can't build a smart AI product if you're calling an LLM an "agent" in the same breath.

Getting the hierarchy right is the difference between shipping something real and shipping a demo.

Save this post so the next time someone mixes them up, you've got the map.

Which of these 7 gets confused the most in your world?
Drop a comment below.

♻️ Repost to help someone in your network finally get the difference.
And follow me (Basia Kubicka) for more on building in the AI era.

TY AI For Leaders for great infographic!

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Greg Zlevor This is a helpful reminder that successful AI projects start with defining the problem first, then choosing the right approach, not the other way around.

Basia Kubicka Author Greg Zlevor Problem first, architecture second being the sequence that the hierarchy is designed to support rather than bypass is the right way to hold this. The layers give you a vocabulary for the second step. The first step still requires human judgment about what you're actually trying to solve.

Ben Gusberg Breaking down these complex technical terms into a simple hierarchy makes a massive difference for everyone. Clear definitions are so helpful when deciding which tech setup fits a specific project.

Basia Kubicka Author Ben Gusberg The decision being about fit rather than capability alone is the frame that makes the hierarchy a practical tool rather than just an educational one. Which layer matches this problem is a different and more useful question than which layer is most advanced.