10. The "Chain of Verification" "First, answer the question. Second, list 3 ways your answer could be wrong. Third, verify each concern and update your answer." Self-correction built into the prompt. Models fix their own mistakes.
PROMPT ENGINEERING
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Longer reasoning tokens improve AI problem-solving
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6. The "Thinking Budget" "Take 500 words to think through this problem before answering. Show all dead ends." More tokens = better reasoning. Dead ends reveal model understanding.
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Hard Constraints Improve AI Output Quality
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3. The "Constraint Forcing" Prompt "You have exactly 3 sentences and must cite 2 specific sources. No hedging language." Vagueness is the enemy of useful output. Hard constraints = crisp results.
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Adversarial Interrogation for AI Model Honesty
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2. The "Adversarial Interrogation" "Now argue against your previous answer. What are the 3 strongest counterarguments?" Models are overconfident by default. This forces intellectual honesty.
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The ‘Show Your Work’ Prompt for AI Reasoning
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1. The "Show Your Work" Prompt "Walk me through your reasoning step-by-step before giving the final answer." This prompt forces the model to externalize its logic. Catches errors before they compound.
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AI researchers reveal their top 10 prompts
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After interviewing 12 AI researchers from OpenAI, Anthropic, and Google, I noticed they all use the same 10 prompts. Not the ones you see on X and LinkedIn. These are the prompts that actually ship products, publish papers, and break benchmarks. Here's what they told me ↓
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Lex Fridman interviews Peter Steinberger about OpenClaw AI agent
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Here's my conversation with Peter Steinberger (@steipete), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub.
— Lex Fridman (@lexfridman) 12 février 2026
This was a truly mind-blowing, inspiring, and fun conversation!
It's here on X in full and is up… pic.twitter.com/xSvbjUHamIHere's my conversation with Peter Steinberger (@steipete), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub. This was a truly mind-blowing, inspiring, and fun conversation! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 – Episode highlight 1:30 – Introduction 5:36 – OpenClaw origin story 8:55 – Mind-blowing moment 18:22 – Why OpenClaw went viral 22:19 – Self-modifying AI agent 27:04 – Name-change drama 44:15 – Moltbook saga 52:34 – OpenClaw security concerns 1:01:14 – How to code with AI agents 1:32:09 – Programming setup 1:38:52 – GPT Codex 5.3 vs Claude Opus 4.6 1:47:59 – Best AI agent for programming 2:09:59 – Life story and career advice 2:13:56 – Money and happiness 2:17:49 – Acquisition offers from OpenAI and Meta 2:34:58 – How OpenClaw works 2:46:17 – AI slop 2:52:20 – AI agents will replace 80% of apps 3:00:57 – Will AI replace programmers? 3:12:57 – Future of OpenClaw community
→ View original post on X — @lexfridman, 2026-02-12 03:17 UTC
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Prompt engineering for AI workflow optimization
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I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to "what do I do Monday morning." Prompt AI
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Using OpenClaw to Improve OpenClaw Usage Recursively
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“it sounds like a lot of what you’re using openclaw for is to improve how you use openclaw” yea