Technique 2: Chain-of-Verification (CoVe) Google's research team uses this method to eliminate hallucinations. The model generates an initial answer, then creates verification questions, answers them, and refines the original response. Template: Task: [your question] Step 1: Provide your…
SAFETY
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Role-Based Constraint Prompting Technique
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Technique 1: Role-Based Constraint Prompting
— God of Prompt (@godofprompt) 5 décembre 2025
The expert don't just ask AI to "write code." They assign expert roles with specific constraints.
Template:
You are a [specific role] with [X years] experience in [domain].
Your task: [specific task]
Constraints: [list 3-5 specific… pic.twitter.com/Ek1YIAZXqOTechnique 1: Role-Based Constraint Prompting The expert don't just ask AI to "write code." They assign expert roles with specific constraints. Template: You are a [specific role] with [X years] experience in [domain].
Your task: [specific task]
Constraints: [list 3-5 specific -
European Strategy Better Internet Kids Safety Innovation
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European strategy for a better internet for kids – BIK+ | Shaping Europe’s digital future https://
digital-strategy.ec.europa.eu/en/policies/st
rategy-better-internet-kids
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#digitaleu #internet #safe #innovation #technology @DigitalEU @ArturHabant @elaniazito @BetaMoroney @mvollmer1 @Ronald_vanLoon @mikeflache @CurieuxExplorer @Shi4Tech -
MIT Mathematics System Ensures Safe Flexible Robot Interactions
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MIT system uses rigorous mathematics to ensure robots flex, adapt, & interact w/people & objects in a safe & precise way. It helps robots remain flexible & responsive w/o exceeding safe force limits: https://
bit.ly/3KynwV0 -
BEHAVIOR Challenge 2025 Winners Announced at NeurIPS
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Are you at #NeurIPS2025 this week? Our colleagues at @StanfordSVL will be announcing the winners of the 2025 BEHAVIOR Challenge, a global competition to stress-test robotic systems against 50 everyday domestic tasks in high-fidelity simulation. Read more:
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Perplexity unveils BrowseSafe-Bench for AI agent security
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Perplexity has introduced BrowseSafe-Bench, a benchmark and fine-tuned detection model to improve the security of browser-based AI agents.
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BrowseSafe Open-Source Tool Hardens Autonomous Agents Against Prompt Injection
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BrowseSafe and BrowseSafe-Bench are fully open-source. Any developer building autonomous agents can immediately harden their systems against prompt injection. Read more:
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BrowseSafe Fine-Tuned Model Outperforms Safety Classifiers
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Our findings show that our fine-tuned BrowseSafe model outperforms both off‑the‑shelf safety classifiers and frontier LLMs used as detectors. These gains are possible through fine-tuning on BrowseSafe-Bench data, allowing us to bypass the reasoning latency of larger models.
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BrowseSafe: Detection Model Against Prompt Injection Attacks
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BrowseSafe is a specialized detection model to defend against evolving prompt injection attacks. It is designed specifically to spot and block malicious instructions hidden in web pages before they can impact AI browser agents.