Technique 5: Specification-Driven Generation Engineers write a spec first, get model agreement, THEN generate. This separates "what to build" from "how to build it" and catches misalignment early. Template: First, write a specification for [task] including:
– Inputs and their
@godofprompt
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Specification-Driven Generation Technique
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Technique 4: Differential Prompting
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Technique 4: Differential Prompting
— God of Prompt (@godofprompt) 10 décembre 2025
Engineers don't ask for one output. They ask for two versions optimized for different criteria, then pick or merge. This exploits the model's ability to hold multiple solution strategies.
Template:
Generate two versions of [output]:
Version… pic.twitter.com/yHkRJkECqf— Technique 4: Differential Prompting Engineers don’t ask for a single output. Instead, they request two versions optimized for different criteria, then select or merge them. This approach leverages the model’s ability to explore multiple solution strategies. Template: Generate two versions of [output]: Version 1:
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[Content optimized for criterion A] Version 2:
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[Content optimized for criterion B] -
Metacognitive Scaffolding for Error Prevention
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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 -
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]
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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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AI Agents Disrupting Financial Workflows
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But the real disruption just started. Airwallex is developing AI agents that handle complete financial workflows without human oversight: invoicing, procurement, treasury, policy enforcement, reconciliation. They're not building better finance software. They're building the
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Meta’s AI Breakthrough Challenges Self-Improvement Narrative
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Holy shit… Meta just dropped a paper that flips the “AI will improve itself and leave us behind” narrative on its head and the implications are massive Here’s the wild part: They argue the safest and fastest path to superintelligence isn’t self-improving AI at all. It’s
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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. -
Gemini vs DeepSeek vs ChatGPT in UI Development
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Here’s the simple takeaway: If you’re building front-end UIs with AI right now, Gemini is on another level. DeepSeek is close. ChatGPT is not even in the race. Crazy how the same “build a site” request exposes the strengths instantly: → ChatGPT thinks like a coder
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