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5 powerful but lesser-known prompt tricks that work with most LLMs

Written by

Eunice van Woezik

in

Here are 5 powerful but lesser-known prompt tricks that work with most LLMs:

  1. Role reversal teaching: Instead of asking the AI to explain something, ask it to pretend you’re teaching it the concept. This often reveals gaps in understanding and gets more precise responses. Example: “Let me verify my understanding—I’ll explain how SQL joins work, and you correct any mistakes I make.”
  2. Incremental refinement chain: Start with a basic output and explicitly build on it through sequential prompts:
    • Tell me the core idea in 5 words
    • Expand the previous response into 2 sentences
    • Now add 3 specific examples
  3. Metacognitive prompting: Ask the AI to explain its reasoning process rather than just the answer: “Walk me through your step-by-step thought process for solving this problem, including any assumptions you’re making.”
  4. Comparative analysis framework: Instead of asking about one thing, frame it as a comparison between multiple items: “Compare and contrast these three approaches, focusing specifically on their trade-offs in terms of [specific criteria]”
  5. Scenario-based constraint setting: Add realistic constraints to get more practical answers: “Solve this assuming you have limited resources and only 2 hours to implement it” or “Explain this to someone who has no technical background and only 5 minutes to understand it”

These techniques help extract more nuanced, practical, and accurate responses from AI systems while maintaining their strengths and working around their limitations.

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