Ever opened ChatGPT to “just ask one quick thing”… and suddenly you're knee-deep in philosophy, GPU conspiracy theories, and a detailed plan to reorganize your entire life?

Same. LLMs don’t think in straight lines — they think in explosions. So here’s how to stop the explosion from taking your whole brain with it.

1. Tell the model what you’re actually trying to do.

Most rabbit holes start because people ask a question like: “Explain X.” Which the model reads as: “Please take me on a 45-minute journey through the history of the universe.”

Try this instead:

• “Explain embeddings because I’m deciding between two project ideas.”
• “I only need a high-level roadmap, no deep dive.”

One sentence of context = 80% fewer side quests.

2. Ask your question from three angles.

The cheapest anti-hallucination trick on earth:

1. Direct: “How does X work?”
2. Inverse: “When does X NOT work?”
3. Compare: “How is X different from Y?”

Triangulation exposes contradictions instantly.

3. Use a mini scaffold so your brain doesn't melt.

Concept → Content → Action

• Concept: What’s the core idea?
• Content: What it looks like in real life.
• Action: What you can do today.

This tiny structure prevents the “information overload → paralysis → cat videos” loop.

4. Let NotebookLM be your thinking mirror.

NotebookLM (or any tool that reads your notes) helps you:

• catch drift in your reasoning
• see logical gaps
• track intent
• avoid self‑inflicted hallucinations

Most “AI confusion” comes from you drifting, not the model.

5. A real example: the classic career-change spiral.

Without structure: You end up debating macroeconomics, childhood trauma, and whether AI will replace jazz musicians.

With structure:

• Intent: “I need clarity, not a TED talk.”
• Cross-check: pros/cons of A and B, failure conditions, overlap
• Scaffold: Concept (stability vs autonomy), Content (daily life), Action (3‑day experiment)

You make a decision instead of philosophically evaporating.

6. The real game

The goal isn’t better prompts. It’s building a thinking environment where hallucinations can’t survive.

Do that, and LLMs stop being tools — they become a second cognitive engine.