Zero → Max AI

EP01 · 8 min

AI vs ML vs Deep Learning vs GenAI vs LLMs

Build a clean category map so you can classify AI products and tasks correctly.

Simple definition
AI is the broad field of getting software to perform tasks that usually need human judgment.
Precise definition
AI is a set of computational methods for prediction, generation, optimization, and decision-making under uncertainty.

Objective

Your goal is to build a map you can reuse for every new AI tool. If the map is fuzzy, later lessons feel random. If the map is clear, you can decide faster.

Visual walkthrough

Start with AI at the top. Inside AI, place Machine Learning. Inside ML, place Deep Learning. Then add Generative AI as a branch that creates new output. Finally place LLMs under GenAI for language tasks.

Worked example (online store)

Our store has four tasks:

  • Filter spam in support inbox.
  • Predict delivery time.
  • Tag review sentiment (positive/neutral/negative).
  • Draft a reply to a customer message.

The first three are predictive ML tasks. The fourth is generative. This single comparison removes most beginner confusion.

Why this distinction matters

If you pick a giant model for a small prediction problem, you pay higher cost and get less controllability. If you use rigid rules for open-ended drafting, outputs feel robotic and brittle.

A correct category decision gives you better speed, lower cost, and easier monitoring.

Quick check guidance

When you answer the quiz below, focus on the task objective first:

  • Predicting a known target? Think classic ML first.
  • Generating natural language? Think GenAI/LLM.
  • Deterministic validation? Rules may be enough.

Three takeaways

  • Categorization is a product decision, not just a technical one.
  • A smaller model plus good framing often beats a bigger model with vague framing.
  • Knowing the map helps you evaluate claims from AI tools critically.

Visual Stage

Interactive walkthrough

Visual map: nested AI categories

Tap each layer to reveal what makes it unique.

Step Insight

AI includes any method that enables software to perform tasks that require judgment, prediction, or adaptation.

Common traps
  • Treating every AI feature as an LLM problem.
  • Assuming generation equals correctness.
  • Ignoring simple non-AI automation when it solves the problem.
Three takeaways
  • AI is the umbrella; ML and deep learning are subsets.
  • GenAI creates outputs, while many ML systems predict labels or values.
  • LLMs are language-focused GenAI models, not the whole AI universe.
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