🚀 Medial Secures Investment on Shark Tank India - Fueling the Future of Professional Social Networking. 🔥

News

Messages

Try our Valuation Calculator →

Back

Rahul Agarwal

Founder | Agentic AI... • 15d

Most people skip guardrails, their AI systems break. I’ve explained in a very simple way below. 1. 𝗜𝗻𝗽𝘂𝘁 & 𝗢𝘂𝘁𝗽𝘂𝘁 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 This is the 𝗲𝗻𝘁𝗿𝘆 𝗮𝗻𝗱 𝗲𝘅𝗶𝘁 𝗰𝗵𝗲𝗰𝗸 of the system. • User input is checked for safety • Harmful, offensive, or unsafe requests are filtered out • After the AI generates a response, it is checked again before showing it to the user This prevents dangerous prompts and unsafe answers from reaching users. _________________ 2. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 These make sure the AI 𝗯𝗲𝗵𝗮𝘃𝗲𝘀 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗼𝗿 𝗮 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝘁𝗮𝘀𝗸. • The system understands what it is allowed to do in a given use case • Clear limits are set so the AI does not go outside its role • It avoids giving misleading or out-of-scope information The same AI behaves differently for healthcare, education, or customer support. _________________ 3. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 This protects the system from 𝗮𝘁𝘁𝗮𝗰𝗸𝘀 𝗮𝗻𝗱 𝗺𝗶𝘀𝘂𝘀𝗲. • They defend against prompt injection and manipulation • They prevent data leaks and false information attacks • They protect both internal systems and external users Without this, AI systems can be tricked into unsafe behavior. _________________ 4. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 These guardrails 𝗰𝗵𝗮𝗻𝗴𝗲 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗮𝘀 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀 𝗰𝗵𝗮𝗻𝗴𝗲. • The system adapts to new rules or policies • It adjusts responses in real time • Legal and ethical rules are always enforced AI systems must stay safe even as laws, users, and risks evolve. _________________ 5. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 These ensure the AI follows 𝗵𝘂𝗺𝗮𝗻 𝘃𝗮𝗹𝘂𝗲𝘀 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀. • Outputs remain socially acceptable • Harmful or offensive content is avoided • Bias in responses is reduced AI should not spread discrimination or harmful ideas. _________________ 6. 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 These ensure the system follows 𝗹𝗮𝘄𝘀 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀. • Industry rules are respected • Data privacy laws are followed • User data is handled safely Non-compliance can lead to legal and financial risk. ✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: • 𝗜𝗻𝗽𝘂𝘁 & 𝗢𝘂𝘁𝗽𝘂𝘁 → what goes in and out • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 → what the AI is allowed to do • 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 → protection from attacks • 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 → real-time control • 𝗘𝘁𝗵𝗶𝗰𝘀 → fair and safe behavior • 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 → legal safety Together, these guardrails make your AI system safe, reliable, and trustworthy. ✅ Repost for others in your network who can benefit from this.

Recommendations from Medial

Vagle

Hakuna matata • 8m

Microsoft engineers spent years building AI Systems were now fired and replaced with the same AI Systems Absolutely ironic 😼

Raju Biswal

🧠 Building Laryaa a... • 1m

Why AI Should Never See What It Doesn’t Need? Most AI systems fail not because they’re unintelligent, but because they see too much. In regulated, real-world environments, visibility is a liability. If an AI needs full screens, raw data, or unres

6 Replies

GOLLAVILLI SANJAYKUMAR

Building in noise is... • 10m

I spent 6 years learning how computers work. Now, computers are learning how humans work — in a matter of days. History shows us: the one who understands a system becomes the master; the system becomes their tool. So the question is — are we becoming

1 Reply

Yashab alam

Founder ZehraSec • 8m

> When LEWIS flexes more features than a Swiss Army knife! From real-time threat detection to self-healing systems—he’s the AI you wish you had in college. ⚔️ LEWIS: Linux Environment Working Intelligence System. Your ultimate AI cybersecurity sidek

Varun Bhambhani
•

Medial • 1m

Social media platform X has officially acknowledged a major lapse in AI content moderation linked to Grok AI. Government sources confirm that over 3,500 posts were blocked and more than 600 accounts deleted. This decision marks an important inflecti

1 Reply

Rahul Agarwal

Founder | Agentic AI... • 3d

Most people even today don't know these skills. I've explained in very simple below. 1. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Learning how to give clear, structured, and goal-driven instructions to AI models to get accurate and useful outputs. Eg: Ch

Rahul Agarwal

Founder | Agentic AI... • 1m

Most people building modern AI systems miss these steps. I've explained each step in a simple way below. 1. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 How multiple AI agents work together as a system. Step-by-step: • 𝗨𝘀𝗲𝗿 𝗥𝗲𝗾𝘂�

2 Replies

Abhinab Pattjoshi

UX without users is ... • 6m

Got a question today: What are AI systems? AI systems learn from training data provided or set by humans. These algorithms adapt to new information, and a knowledge base supports the AI in attempting to solve the queries it receives. If the AI does n

Shrrinath Navghane
•

NexLabs • 4m

Question for you: Where would you want a human-in-the-loop in an AI matchmaking system? What would make you trust AI-driven play? In multiplayer gaming, trust matters more than speed or cost. Here’s how we’re building AI-native matchmaking for Redou

AKHIL MISHRA

Helping businesses g... • 1m

Everyone wants AI agents. Very few enterprises can actually run them. Here’s the uncomfortable truth: AI agents don’t fail because they’re not smart. They fail because organizations aren’t ready. In demos, agents look cheap. In production, costs e