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Day 1: The mental model that changes everything

Most AI courses start in the wrong place.

They teach prompt hacks before explaining why prompts work.
So you copy templates instead of adapting to real problems.

Today, we fix that.

Once this foundation clicks, you’ll never look at AI the same way again.

How AI actually reads your words

When you type: “The bank was steep”

The model has to decide are you talking about money or a riverbank?

It uses something called attention.
Basically, it looks at surrounding words and asks:
“What context helps me understand this?”

That one idea explains why 80% of prompts fail or succeed.

Clear context = better output.
Weak context = guessing.

You’ve felt this already.
Two similar prompts. One great answer. One trash answer.
The difference is almost always context clarity.

The one setting that changes everything

Temperature controls randomness on a 0-to-1 scale

At 0 the model gives you its most confident answer every time, at 1 it takes creative risks

Set it low for factual queries and analysis, push it higher when you want unexpected ideas

This single parameter separates frustrating AI sessions from productive ones, most people never touch it and wonder why their results feel random

Try this right now: run the exact same prompt twice at temperature 0, you’ll get nearly identical outputs, then run it at temperature 1 and watch how different each generation becomes

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Why AI lies to you and how to catch it

Here’s something counterintuitive: AI doesn’t know what’s true

It predicts what text is likely to come next based on patterns, and confident-sounding text patterns exist for both facts and fiction, so the model produces both with equal confidence

Studies show nearly half of AI-generated citations are partially or completely fabricated... the model invents author names, journal titles, even URLs that don’t exist

The fix isn’t hoping they’ll patch this, hallucination is structural, not a bug

Instead: verify specific claims, use low temperature for factual queries, ask the model to acknowledge uncertainty, and later this week you’ll build RAG systems that ground responses in real documents to eliminate this problem entirely

The model landscape: know your tools

The “best” model changes based on what you’re doing, and using the wrong one for your task is like using a screwdriver as a hammer

Here’s how the landscape breaks down right now:

Claude from Anthropic owns coding, marketing/long-form writing, and spreadsheet analysis... Claude Opus 4.5 leads the benchmarks and the community feedback, and the Claude in Excel integration alone is worth the subscription for anyone spending more than an hour per week in spreadsheets

Gemini 3 Pro from Google dominates research... that 1M token context window means you can upload an entire research corpus, a full codebase, months of meeting transcripts, and Gemini holds all of it while answering questions with full context, plus native Google Search integration pulls current information rather than hallucinating

GPT-5 is a useful negative example... it consistently produces the most generic, obviously-AI-written output, understanding what mediocre AI output looks like helps you avoid producing it

Grok for real-time social analysis on X, limited use case but nothing else does it as well

The decision rule

Stop asking: “Which AI is best?”
Start asking: “What am I trying to do?”

• Coding / technical writing → Claude

• Research + current info → Gemini

• Long document analysis → Gemini

• Marketing & brand voice → Claude

• Spreadsheet work → Claude + Excel

• Social analysis → Grok

• Images → Nano Banana Pro

• Video → VEO 3.1 or Kling 2.6

This removes decision paralysis—and helps you actually master tools instead of jumping between them.

Today’s assignment

If you don’t already have them, sign up for Claude and Gemini.

Run the same prompt on both. Change temperature settings. Watch how outputs change.

Internalize this mental model.

Everything else builds on this.

Stay curious,

CoolDeep AI
Helping you stay ahead with AI & productivity