AI 102: AI is prediction, not intelligence. And how you can start using it Today!

TL;DR

This article will give you three practical, real-world examples of how AI works, and more importantly, how you can use it to do higher-quality work in less time.

AI Is prediction, not Intelligence

Every modern AI model — ChatGPT, Claude, Gemini — is based on one idea:

It predicts what should come next based on everything it has seen.

That’s it. Just prediction.

• Not thinking
• Not understanding
• Not reasoning like a human

And while that may sound simple, it’s powerful enough to help you write, plan, synthesize, critique, design, generate images, and build software faster than ever before.

If you enjoy this sampler, you can taste the whole enchilada at the end of the article, where I explain why AI behaves this way and how to use that knowledge to your advantage.

Before we dive into the practical examples, there’s one skill that will dramatically improve your results.

A quick note on AI Prompting Frameworks (And why they matter)

A prompting framework is simply a structured way of asking AI to perform a task. Think of them as reusable recipes that help the model predict better outcomes.

AI frameworks matter because they:

• Reduce ambiguity
• Help the model lock onto the right pattern
• Produce consistent, repeatable output
• Force you to clarify what you actually want

If AI were a cook with access to all the ingredients in the world, then you are the Head Chef, using frameworks as recipes to tell AI what to cook.

In this article, I’ve chosen one framework for each example:

• ChatGPT (Case 1): Task → Structure → Output
• Claude (Case 2): Critique → Risks → Mitigations
• Gemini Image Generation (Case 3): No textual framework, because image models respond to visual constraints, not logical ones

Now that you have the mental model and the toolset, let’s get practical.

Case 1: ChatGPT — Turning a meeting summary into an Action Plan

One of the easiest ways to see AI as prediction is this:

Give ChatGPT a messy, narrative description of a meeting and ask it to turn it into an action plan.

Why this works:
ChatGPT has seen millions of examples of how teams structure responsibilities, deadlines, risks, and decisions. So when you give it a meeting paragraph, it predicts the shape of a “good” action plan from similar patterns.

The Framework: Task, Structure, Output

You give ChatGPT three things:

1. The task: Turn this meeting summary into an action plan
2. The structure: Deliverable, deadlines, tasks, risks
3. The output: A final formatted action plan

This gives ChatGPT a pattern — a shape — to predict into.

Example workflow

Your input might look like this:

Turn this meeting summary into an action plan.
Structure: Deliverable, deadline, tasks by owner, blockers, next sync, risks.

We met with the marketing team today. They want a landing page by Monday. I told them design is blocked because we don’t have final copy. Sarah will draft it tomorrow. I also need to check if our analytics script is firing. Oh, and someone needs to update the hero image because it’s outdated.

ChatGPT’s expected output should look something like this:

• Deliverable: New landing page
• Deadline: Monday
• Tasks:
  • Sarah → Draft final copy by tomorrow
  • You → Verify analytics script is firing
  • Design → Update hero image
• Blocked by: Missing final copy
• Next sync: Friday
• Risks: Delays in copy, unclear approval process

ChatGPT didn’t “understand” your meeting. It predicted the structure of a meeting action plan.

ChatGPT is your “structure generator” — it takes something unstructured and predicts the structure it should follow.

Case 2: Claude — Ask it to critique your plan (The Risk Management Framework)

Claude is exceptional at patterned skepticism.
If ChatGPT predicts structure, Claude predicts failure modes.

When you give Claude a plan and ask it to critique it, it draws from patterns in:

• Audits
• Enterprise risk assessments
• Legal reasoning
• Compliance frameworks
• Post-mortems
• Professional analyses

As a result, it spots blind spots you didn’t see.

The Framework: Critique, Risks, Mitigations

Traditionally known as a Risk Management Framework, this provides a structured approach to identifying, assessing, and mitigating risks.

Claude shines with frameworks like:

1. Critique: What’s unclear or problematic
2. Risks: What could go wrong
3. Mitigations: How to reduce or avoid the risk

Example workflow

Your input:

Claude, critique this plan using the Critique, Risks, Mitigations framework.

Launch the new onboarding prototype by Wednesday. Marketing will announce it Thursday. We’re still unsure about analytics integration but should be fine. Design hasn’t approved the new flow, but we’ll fix it after launch.

Claude’s expected output might include:

• Critique:
  Dependencies are unclear. Approval flow is missing. Analytics status is ambiguous.

• Risks:

  • Missing analytics data blocks evaluation
  • Launching without design approval introduces inconsistency
  • Marketing timeline assumes a stable Wednesday release

• Mitigations:

  • Confirm analytics status 24 hours before launch
  • Get asynchronous sign-off from design
  • Use a staged rollout

Claude is your “professional critique partner” — it predicts what tends to go wrong.

Case 3: Gemini — Visual prediction with the Nano Banana image model

Gemini’s strength becomes obvious when you use Nano Banana for image generation.
Nano Banana is simply the name of the model used to generate images.

Why image models don’t use text-based frameworks

Text frameworks work because text generation relies on logical prediction.
Image models rely on visual probability spaces.

Words like “role”, “steps”, “evaluate”, or “structure” do not help an image model. Instead, they respond to:

• Composition
• Lighting
• Style
• Subject
• Mood
• Reference patterns

So for Gemini’s example, we use a visual prompting approach, not a multi-step text structure.

If ChatGPT is great at predicting structure, and Claude is great at predicting risk, Gemini’s Nano Banana model predicts visuals.

Example Workflow (Visual Prompting)

Prompt:

A realistic photo of a person working on a laptop at night, with a city skyline in the background. Cinematic lighting, shallow depth of field, warm tones.

Gemini predicts:

• The typical elements of cinematic lighting
• The color palette of nighttime photography
• The framing patterns of DSLR portrait shots
• The visual conventions of “person + laptop + skyline”

Your edits (“make it moodier”, “change the angle”, “add reflections”) simply reshape the probability space.

Gemini is your “visual synthesizer” — the model that predicts a photograph from familiar ingredients.

I asked ran the prompt above for you and it generated this image

Why AI prediction feels like intelligence (The whole enchilada)

You’ve now seen prediction in three forms:

• Structured prediction: ChatGPT
• Analytical prediction: Claude
• Visual prediction: Gemini

So why does prediction look like intelligence?

Because when you give an AI model:

• Enough examples
• Enough context
• Enough parameters
• And strong enough training signals

Prediction eventually becomes indistinguishable from structured problem-solving — especially when you use frameworks.

Here’s the deeper mental model.

1. AI predicts in words — tokens — not ideas

Models generate text or pixels one piece at a time, choosing the most probable continuation.

If you say “Old MacDonald,” ChatGPT will most likely answer “had a farm, Ee i ee i oh!”

There is no inner world, no reasoning, no plan — just probability.

2. Patterns Become Capabilities

Because everyday work follows patterns — how we write emails, structure meetings, design interfaces, review risks — AI becomes extremely good at reproducing those patterns.

3. The Illusion of Thought

When your brain sees a structured output like:

• A project plan
• A risk assessment
• A photo
• A helpful explanation

It interprets it as thought.
But the model is simply predicting a pattern that has worked before. Most of the time it does really good work. Sometimes it needs adult supervision.

4. Mastery comes from enabling AI to do what it does best

When you understand that AI only predicts:

• You stop trying to make it “think”
• You start shaping better constraints
• You become a better orchestrator
• Your results improve dramatically

AI doesn’t replace thinking. It replaces:

• The blank page problem
• The messy draft problem
• The scattered notes problem
• The visual exploration problem

You still choose the destination.
AI just drives the car.

If you learn to collaborate with prediction — not fight it — you’ll produce more, better, and faster, without losing your voice or judgment.