Technique 3: Metacognitive Scaffolding Instead of asking for an answer, engineers ask the model to explain its reasoning process BEFORE generating. This catches logical errors at the planning stage. Template: Before you [generate output], first:
1. List 3 assumptions you're
PROMPT ENGINEERING
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Metacognitive Scaffolding for Error Prevention
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Multi-Shot Technique with Failure Cases
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Technique 2: Multi-Shot with Failure Cases Everyone uses examples. Engineers show the model what NOT to do. This creates boundaries that few-shot alone can't establish. Template: Task: [what you want] Good example:
[correct output] Bad example:
[incorrect output]
Reason it -
Constraint Techniques for Prompts
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Technique 1: Constraint-Based Prompting Most prompts are too open-ended. Engineers add strict constraints that force the model into a narrower solution space, eliminating 80% of poor outputs before they occur. Template: Generate [output] with these non-negotiable constraints:
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5 Techniques for Production-Grade AI Prompts
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Top engineers at OpenAI, Anthropic, and Google don't prompt like you do. They use 5 techniques that turn mediocre outputs into production-grade results. I spent 3 weeks reverse-engineering their methods. Here's what actually works (steal the prompts + techniques)
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Top AI Models for Different Tasks: A Practical Guide
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ChatLLM model picks we keep coming back to: Everyday → GPT- 5.1 Coding → Sonnet 4.5 / Opus 4.5
Images → Nano Banana Pro, Midjourney → Sora 2, Seedance Pro
TTS → ElevenLabs, Hume
Fast tasks → Gemini Flash
Reasoning → GPT-5.1 Thinking Model switching handled -
Clarification on AI Prompt Techniques and Expert Positioning Methods
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A good chunk of people misunderstood this tweet btw, which is my bad. I am not suggesting people use the old style promoting techniques of “you are an expert swift programmer” or etc. it’s ok.
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Gemini 3.0 and GPT-5 Codex Max for Full Stack Development
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Pro Tip – Use Gemini 3.0 for visual understanding and front-end coding Combine with GPT-5 Codex Max for backend!
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Nested Simulation Efficiency in AI Model Computing
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I could certainly imagine that "nesting" the simulation might be too "effortful" for the model, compute or data density wise. My results with it are not too bad so imo it's at least worth people try / experiment with / think about. For example it might be useful to read multiple
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AI Comparison: The Psychopath Method
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I saw a guy prompting today.
Tab 1 Claude.
Tab 2 Gemini.
Tab 3 ChatGPT.
Tab 4 Grok.
Tab 5 DeepSeek.
He asked every AI the same exact question.
Hit run on all five.
Patiently waited, then compared each model’s response. Picked the best one.
Like a psychopath. It's me. -
Poetiq Dominates ARC-AGI-2 Benchmark; New AI Tools Released
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Top stories in AI today: – Poetiq tops ARC-AGI-2 with Gemini variant
– The Rundown Roundtable: Our AI use cases
– Reverse engineer ads in minutes
– Poetry prompts can bypass AI guardrails
– 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/poetiq-crack
s-major-reasoning-benchmark
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