Paper: "Quantifying Human-AI Synergy" by Riedl & Weidmann, 2025 https://openreview.net/pdf/8b876352f6a3393c76a956a97f940c0512e38671.pdf
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SAFETY
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Study on Human-AI Synergy in 2025
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Non-biological AGI: The Ultimate Source of Global Power
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Technology is power — and a non-biological general intelligence is the most profound expression of that in history. Whoever controls intelligence becomes the arbiter of power, because everything flows downstream from it:
economic strength, military capability, scientific -
Claude’s Post-Training Excellence and Alignment in AI Models
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Every time you are chatting with claude(Opus 4.5 esp. but seems to apply to all models), you quickly realize how clever claude post-training is. The attention to details, data quality. It's rare that claude will break in the middle of conversations. I think this is beyond system prompt and training dynamics/physics, and more about aura-like things: behaviors, personalities, when to push back/when to defer, and countless subtle edge cases. It's no surprise alignment people there follow closely the training. There was an invited talk at CMU this past spring on alignment and I asked why claude vibes and post-training feel different, the answer was high-level as you'd expect, but same: training, post-training, alignment, and evals teams are closed-loop. Sam Bowman (@sleepinyourhat) From everything we know so far, Opus 4.5 seems to be the best-aligned model out there in a bunch of ways. I follow the training process closely as part of my work on alignment evaluations. Here's my guess about the two things that are most responsible for making 4.5 special. 🧵 — https://nitter.net/sleepinyourhat/status/1997006353647522098#m
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Techniques to Reduce Hallucinations and Boost Accuracy
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You can use these techniques RIGHT NOW I've been using these with Claude Sonnet 4.5, GPT-4, and Gemini 2.0 Flash for 6 months. Results: – 100% reduction in hallucinations on technical docs
– 3x faster iteration on code generation
– 90%+ accuracy on complex analysis tasks The -
Meta-Prompting: The Nuclear Option
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Technique 10: Meta-Prompting (The Nuclear Option) This is what OpenAI's red team uses to break their own models and find edge cases. You ask the AI to generate the perfect prompt for itself. Template: I need to accomplish: [high-level goal] Your task:
1. Analyze what would -
Multi-Perspective Prompting Technique
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Technique 9: Multi-Perspective Prompting Anthropic's Constitutional AI uses multiple viewpoints to reduce bias and improve reasoning. Template: Analyze [topic/problem] from these perspectives: [PERSPECTIVE 1: Technical Feasibility]
[specific lens] [PERSPECTIVE 2: Business -
Context Injection with Boundaries
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Technique 6: Context Injection with Boundaries Anthropic engineers inject massive context but set clear boundaries on what matters. Template: [CONTEXT]
[paste your documentation, code, research paper] [FOCUS]
Only use information from CONTEXT to answer. If the answer isn't in -
Technique 5: Confidence-Weighted Prompting
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Technique 5: Confidence-Weighted Prompting Google DeepMind uses this technique for high-stakes decisions. Ask the model to rate its confidence and provide alternative answers. Template: Answer this question: [question] For your answer, provide:
1. Your primary answer
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Structured Thinking Protocol for GPT-5
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Technique 4: Structured Thinking Protocol
— God of Prompt (@godofprompt) 5 décembre 2025
OpenAI's GPT-5 team uses this for complex reasoning tasks.
Force the model to think in layers before responding.
Template:
Before answering, complete these steps:
[UNDERSTAND]
– Restate the problem in your own words
– Identify what's… pic.twitter.com/yFXgwVm5acTechnique 4: Structured Thinking Protocol OpenAI's GPT-5 team uses this for complex reasoning tasks. Force the model to think in layers before responding. Template: Before answering, complete these steps: [UNDERSTAND]
– Restate the problem in your own words
– Identify what's… -
Few-Shot Learning with Negative Examples
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Technique 3: Few-Shot with Negative Examples Anthropic discovered that showing the model what NOT to do is as powerful as showing what TO do. Template: I need you to [task]. Here are examples: GOOD Example 1: [example] GOOD Example 2: [example] BAD Example 1: