solid approach, love the self-check and engagement with the llm, prompts hit different when they're clear
LLMS
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Directing Intelligence: New Frontier, Prompting as Cheat Code
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clean take, directing intelligence is the new frontier, coding's still useful but prompting's the cheat code
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Master 4 Prompt Frameworks for Shockingly Good AI Outputs
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Prompt frameworks are the difference between “AI feels mid” and “AI feels magical.” Start with these 4: 1. APE
2. PECRA
3. OSCAR
4. TAG Master them → and AI will finally deliver shockingly good outputs. -
Frameworks for Efficient AI Prompting at Scale
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Frameworks also make AI work for you at scale: Want 20 tweets? Use PECRA.
Want research with critique? Use OSCAR.
Want copy tailored to an audience? Use TAG.
Want clarity in goals? Use APE. No more “re-rolling” prompts endlessly. -
AI as Engine, Prompt as Steering Wheel: 4 Best Frameworks
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AI is like an engine. Your prompt is the steering wheel. But without a framework, you’re just spinning it randomly. Frameworks turn prompting into a repeatable system. Let’s break down 4 of the best:
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AI prompt frameworks: Avoid the costly mistake of assuming it knows your wants.
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The most expensive mistake in AI: assuming it knows what you want. AI is incredibly capable but terrible at mind-reading. Here are 4 frameworks for writing prompts to get shockingly good results:
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GPT-5 Energy Consumption: 18 Wh Per Response Analysis
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GPT-5 consommerait 18 Wh par réponse. Huit fois plus que GPT-4.
Et bien loin des 0,34 Wh annoncés par OpenAI, puisque GPT-4 atteindrait en réalité 2,12 Wh selon l’étude de l’Université de Rhode Island. Au delà du chiffre, et de sa méthode de calcul par hypothèse, c'est une -
Chain-of-Thought: Underrated tip that doubled output
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Chain-of-Thought literally doubled my output. Underrated tip.
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Efficiency improvements keeping pace with model demand and quality
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Yes, I think the efficiency improvements are doing a pretty good job of keeping up with demand (and what people care about is not necessarily bigger models, but better models).
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Reasoning Systems, Not LLMs, Perform Mathematical Tasks
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The LLMs aren't doing math. The reasoning systems on top of them are.