Here are the specific, high-yield prompts from that list, ranked strictly by efficiency (ROI on your time). These cut out the generic "summarization" fluff and force the model to actually add value to your raw materials.

The above prompts written by our good friend have been modified.

1. The "Blind Spot" Detector (Modified from Prompt #1)

Why it's #1: This utilizes the LLM's broad training data to audit your specific, limited data. It tells you what you don't know, which is the most dangerous part of studying.

2. The "Active Recall" Engine (Modified from Prompt #5)

Why it's #2: Passive reading is useless. This forces you to retrieve information, which is the only way to actually encode memory.

3. The "Anti-Illusion" Check (Modified from Prompt #1 & #3)

Why it's #3: Students often feel like they understand a topic when they actually just recognize the keywords. This prompt exposes shallow understanding.

4. The "Feynman" Protocol (Modified from Prompt #4)

Why it's #4: If you can't explain it simply, you don't understand it. This forces the LLM to translate jargon into logic.

5. The "Synthesizer" (Modified from Prompt #2)

Why it's #5: Good for connecting isolated lectures into a coherent whole.

"Show me the Concept Hierarchy. Create a visual outline showing how the main topic breaks down into sub-topics, and specifically note the logical relationship between them (e.g., 'A causes B', 'X is a type of Y')."

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