How to Ask AI the Right Questions: The Art of Prompt Engineering

🚀 Unlocking the Power of AI with Prompt Engineering! 🤖✨ In today’s world, AI models are only as good as the prompts we give them. That’s where prompt engineering comes in — the art and science of crafting precise, clear, and context-aware instructions to get the best results from AI tools.

GPT-5 comes with a new feature: scheduled notifications. Until now, AI interactions were linear and synchronous — I asked a question, it answered. With notifications, I can set it to operate asynchronously, delivering something independently on a schedule. For example, I’ve scheduled one for 7:00 each morning. GPT-5 generates a fresh insight on one of my ongoing projects, drawn from the body of work we’ve been building together. It might highlight a blind spot, suggest a connection across projects, or surface a thread I had set aside. This marks a meaningful shift in how AI can be used. It’s no longer just a tool I query, but an autonomous partner integrated into my workflow, helping to sustain momentum.

How do you create truly useful AI? To find answers, Nancy Wang dove into Pascal BORNET's five-level framework for matching agentic capabilities with real-world problems. From simple task automation to fully autonomous decision-making, understanding these levels of agentic delivery is crucial for any organization looking to implement AI effectively. The key? It's not about flashy tech — it's about matching the right type of AI to your specific business needs. Whether you're just starting your AI journey or scaling existing solutions, our analysis breaks down each level with practical examples and implementation strategies that move beyond hype to deliver results. See the piece here: https://lnkd.in/eKZxxnsq #AI #Automation #DigitalTransformation #BusinessStrategy #ArtificialIntelligence

My friend and co-founder Nancy Wang spent some time lately reading Pascal BORNET latest book on Agentic Artificial Intelligence. An excellent read to contextualize GenAI agents in a company, then frame, and execute on the opportunities they offer. However, at 572 pages, that's quite a read 😅 So she summarized some of her main takeaways in to a blog post, that she hopes could help you too️! ⬇️

How do you move from AI hype to AI that actually works? I explored Pascal BORNET's five-level framework for agentic AI — from basic task automation to fully autonomous decision-making. The insight that struck me most and what we are experiencing every day: Success isn't about implementing the flashiest technology. It's about matching AI capabilities to your specific business challenges. Ready to build AI that truly serves your business? #AI #Automation #DigitalTransformation #BusinessStrategy #ArtificialIntelligence #AIthatworks

🌟 Excited to share an in-depth look at DeepSeek’s V3.1 AI model on my blog! 🚀 This open-source powerhouse features a significant 128K token context window, hybrid inference modes, and advanced agent capabilities, pushing AI boundaries. Whether analyzing extensive documents or optimizing coding tasks, V3.1 is a game-changer for developers and businesses. Curious how this tool can transform your workflows? Delve into the complete article for insights, code snippets, and practical advice drawn from my hands-on AI implementation experience in corporate settings. 💡 Pro Tip: Explore V3.1 on Hugging Face to unlock its full potential for your projects. ❓ How are you leveraging AI to innovate in your work? Share your thoughts below! 🔗 Check out the full article: [https://lnkd.in/grq7viaj] #AI #MachineLearning #DeepSeek #OpenSource #TechInnovation

To build more reliable AI, we're using a two-step "contractor-prosecutor" model. The first model proposes a structured decision with exact evidence, while a second model acts as a prosecutor, trying to find flaws based on domain rules. This adversarial duet embeds critical thinking into the workflow and turns every mistake into an auditable artifact. 💡

The RICE scoring model is broken for AI features. Here's why: - 𝐑𝐞𝐚𝐜𝐡: AI scales unpredictably - 𝐈𝐦𝐩𝐚𝐜𝐭: Hard to measure at the start - 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞: Lower with new tech - 𝐄𝐟𝐟𝐨𝐫𝐭: Changes as you learn Smart PMs are switching to the 𝐃𝐈𝐕𝐄 framework: - 𝐃ata availability - 𝐈teration speed - 𝐕alue hypothesis - 𝐄xperiment cost Which frameworks are you using for AI prioritization?

🚀 Exciting news in the world of AI! Ever wondered how a simple prompt rewrite could dramatically enhance performance? Our latest blog post dives into the Tau Benchmark and reveals how we boosted GPT-5-Mini's results by a whopping 22%. Yes, you read that right—a little tweak can lead to big gains! If you're in the business of optimizing AI models (or just love a good success story), don’t miss this one. Check it out here: https://ift.tt/BUs6L1e

I love this perspective. We often forget that AI is a tool designed to make life easier. It should take on the mundane so that we can focus on the creative. But it leaves me wondering: How often do we use AI in L&D in the opposite way? Are we building systems that automate creativity rather than administration? And if so, what might we be losing in the process?