Five prompts that separate people who understand a field from people who have memorized a field. Originally shared by 溪树知识 on Douyin, these aren’t about getting AI to summarize — they’re about using AI to stress-test your understanding by mapping expert mental models, finding disagreements, and exposing the gap between surface knowledge and deep comprehension.
| *Source: 溪树知识 on Douyin (Episode 94) | Medium: 5 AI Prompts for Fast-Track Learning* |
The Five Prompts
Prompt 1: Map the Expert Mental Models
“What are the 5 core mental models that every expert in this field shares?”
This isn’t asking “summarize this subject” or “explain this concept.” It’s asking for the mental frameworks — the structures that experts build over decades that let them see patterns instantly. These are the models that professors spend ten years forming.
Example: Ask this about machine learning, and you don’t get “neural networks are layers” — you get the mental models like “bias-variance trade-off thinking,” “data distribution awareness,” and “regularization as skepticism.”
Prompt 2: Find Where Experts Disagree
“Show me the 3 places where experts in this field fundamentally disagree, and what each side’s strongest argument is.”
This is a shortcut to intellectual maturity. In any mature field, experts disagree on fundamentals. Knowing where they disagree and why gives you a map of the field’s intellectual landscape — where consensus exists, where it’s contested, and where the frontier is.
Within 20 minutes, you get a map of the entire field’s intellectual territory: what’s shared consensus, what’s contested, and what’s unsolved.
Prompt 3: Generate the Understanding Test
“Generate 10 questions that would expose whether someone deeply understands this subject versus someone who just memorized facts.”
This is self-assessment. The questions AI generates will test for transfer — can you apply concepts in unfamiliar contexts? Can you explain why, not just what? Each question should make someone who merely memorized uncomfortable.
Use it as a self-test: spend 6 hours with the original material, then answer these 10 questions. For each wrong answer, ask: “Explain why this is wrong and what I’m missing.”
Prompt 4: Expose Your Blind Spots
“Tell me what’s wrong with my understanding and what I’m missing.”
After working through the first three prompts, share your synthesis with AI and ask it to attack your understanding. This creates a feedback loop that’s hard to get otherwise — most people won’t tell you what you’re getting wrong, but AI will.
Prompt 5: Build the Knowledge Map
“What are the prerequisite concepts I need to understand first, and what should I learn after this?”
This positions your current learning within the broader field. It prevents the common trap of learning topics in isolation without understanding how they connect.
Why These Work
Traditional learning:
Read → Summarize → Feel confident → Forget
"What does the field say?"
These five prompts:
Map → Disagree → Test → Attack → Connect
"How does the field THINK?"
The shift is from content (what to know) to structure (how to think). You’re not asking AI to teach you facts — you’re asking it to reveal the architecture of expert thinking so you can build the same mental structures.
Practical Workflow
| Step | Time | What to Do |
|---|---|---|
| 1 | 10 min | Run Prompt 1 — map the mental models |
| 2 | 10 min | Run Prompt 2 — find the disagreements |
| 3 | 15 min | Run Prompt 3 — take the understanding test |
| 4 | 10 min | Run Prompt 4 — get your blind spots exposed |
| 5 | 5 min | Run Prompt 5 — build the learning path forward |
Total: ~50 minutes to go from “I’ve heard of this” to “I understand the structure of this field.”
For Educators
These prompts can be assigned as homework. Instead of “read Chapter 3 and summarize,” try: “Use these five prompts on Chapter 3’s topic, then write a one-page reflection on what surprised you.” Students engage with the structure of knowledge, not just its surface.
How LearnAI Team Could Use This
- Course assignments — Turn passive reading into active field-mapping homework: students identify mental models, expert disagreements, and prerequisite chains before class discussion.
- Research onboarding — Give new LearnAI contributors a repeatable prompt sequence for quickly understanding unfamiliar domains.
- Office hours and tutoring — Use the blind-spot prompt to diagnose where a learner’s explanation is shallow or missing prerequisites.
- Curriculum design — Use the knowledge-map prompt to sequence modules from prerequisites to advanced follow-up topics.
Real-World Use Cases
- Self-study plan creation — Map prerequisites and next topics before committing weeks to a course.
- Exam preparation — Understanding-test questions reveal whether students can transfer concepts instead of reciting definitions.
- Professional upskilling — Engineers entering a new field identify expert mental models and live disagreements.
- Teacher lesson planning — Turn a chapter into discussion prompts, misconception checks, and extension topics.
- Research literature entry — Use disagreement and blind-spot prompts to locate open questions and contested assumptions.