#通用技法
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The Feynman Technique: Explain Any Hard Concept So a 12-Year-Old Gets It
Use the Feynman Technique to have AI teach you: first explain it clearly in plain language with an analogy, then flag the point you're most likely to misunderstand, and finish with a self-check.
Decision Matrix: Get Unstuck Between Two or Three Options
Hand the AI the options you're torn between; it lists the key considerations, gives weighted scores, points out risks you might have missed, and gives a reasoned recommendation.
AI Agent Task Spec: Turn Vague Requests into Executable Instructions
2026 has entered the agent era — this template turns 'I want X' into an executable spec for an agent: goal, steps, available tools, definition of done, and boundaries.
Meeting Notes to Action Items: Paste a Transcript, Get Who/What/When
Turns a messy meeting transcript or notes into decisions, action items (owner + deadline), and open questions — done in 30 seconds after the meeting.
Gemini 2.5 Pro: Drop a Screenshot, Get Your Next Steps
Uses Gemini 2.5 Pro's multimodal ability to turn any screenshot — an error message, an admin dashboard, a report, a conversation — into a concrete, actionable next-step checklist.
Context Engineering: What Separates You in 2026 Isn't the Incantation, It's the Ingredients
Feed the AI structured background, what's already been tried, constraints, and available tools/data structures before giving it the task — in 2026 the real leverage point has shifted from 'prompt tricks' to 'context.'
Prompt Chaining: Split a Big Task into Three Steps for Better Quality
Don't feed an entire complex task into a single prompt — split it into three separate steps ('decompose → produce each part → integrate'), and the output is noticeably more stable and complete.
Self-Critique Revision Loop: Draft, Self-Criticize, Revise — Three Rounds of Polish in One Prompt
Has the AI produce a first draft, then switch into a strict editor role to pick apart its own flaws, and finally rewrite based on that critique — effectively running three rounds of polish inside a single prompt.
Rubric-Driven Output: Tell AI What a Perfect Score Looks Like First
Write out the grading rubric in your head and give it to the AI first — it will use that yardstick to self-align, self-evaluate, and fix whatever falls short, getting you much closer to the quality you actually want.
The Anti-Goal Sandwich: One Line to Stop AI's Most Common Mistake
Explicitly naming "the most common failure mode AI makes on this type of task" in the prompt and telling it to proactively avoid that mode — 2026 field tests show roughly a 28-30% reduction in errors.
Contrastive Example Calibration: Show It What You Want AND What You Don't — One Negative Example Beats Three Positive Ones
Demonstrate both the 'desired output' and the 'output that shouldn't appear' at the same time, so the AI calibrates between the two poles; for tasks about tone and precision, a single negative example often outperforms three positive ones.
Chain-of-Thought Scaffold: A Fixed 5-Step "Think Before You Answer" Process
Forces the AI to think through 5 fixed steps before answering — restate → list assumptions → reason step by step → self-check → conclude. Field tests show roughly a 30% reduction in confidently wrong answers on complex reasoning tasks.