GPT-5 introduces scheduled notifications for asynchronous AI interactions

GPT-6 plans unveiled by Sam Altman! Unlike older models, GPT-6 isn’t just smarter, it’s personal. From writing to planning, it learns your style, recalls past chats, and acts like a true digital companion. The future of AI is not just about answers, it’s about understanding you better every day. 👉 Curious how GPT-6 could change your world? Check out the slides! Follow bizzzup for more AI related updates! #GPT6 #SamAltman #AIUpdate #AIRevolution #TechNews #FutureTech #SmartAI

Mastering AI comes from building your system, not hacks. Document wins, refine failures. Reuse proven prompts instead of reinventing them. Compare across models (Claude, GPT, Gemini, Grok) Use the ones you pay for most, use the others as checks and balances for your main set. Treat AI like a teammate—ask, refine, correct, feedback and document everything. Track model changes—what worked in GPT-4 won’t always work in GPT-5.

Think of prompt engineering as giving directions: the clearer the map, the smoother the journey. That’s why it matters. Prompt engineering isn’t a mystery.it’s simply the art of asking AI the right questions in the right way. Clear, structured prompts lead to better answers, making it an essential skill for anyone who wants to use AI effectively.

📝 It’s All About the Prompt — And Mastery Starts Here Prompting isn’t just a way to talk to AI—it’s a strategic skill that defines the quality of AI output. With GPT-5’s enhanced reasoning and context retention, well-crafted prompts are now more powerful than ever. Here are our top tips for better prompts: - Set clear context & goals to guide reasoning - Use step-by-step structure to avoid tangents - Add style or persona cues for tone control - Include examples to shape format and depth - Frame with safety to ensure reliable, compliant responses Prompting isn’t a fad—it’s a core AI competency. Ready to level up your prompting game? 👉 Read more: https://lnkd.in/emhVftDj

Generative AI offers immense potential, but its tendency to produce errors and inconsistencies creates real challenges. Amazon’s Catalog AI combines AI-driven detection with human feedback to improve content quality at scale. This article explores how businesses can implement similar strategies to ensure reliable AI outputs. Read the full article here: https://bit.ly/4mU0HJ1 #genAI #strategies #innovation #implementation #AIoutputs

By announcing that GPT-6 will be shifting to sustained and personalized interaction, Sam Altman of OpenAI is taking a memory-backed approach to make AI more trustworthy and context-aware. At InstaLILY AI, we believe context enables trust, and trust enables delegation. 🤝 Our AI Teammates are trained on your industry, embedded in your stack, and orchestrated to execute full workflows autonomously.  They persist context across systems tracking: • Ticket history • Quote status • Inventory checks • Prior part validations. They don’t start from zero each time. Since our agents are architected to be context-aware and history-informed, they are capable of consistent execution across time and systems. In a world that increasingly wants AI to execute, memory is more than a nice-to-have, it's the unlock. To learn more, check out the article in the comments.

🚀 Just published: Unlocking the Power of Memory-augmented Conversational Platform (MCP)! Most AI chatbots forget as soon as the chat ends. But with MCP, your AI remembers, reasons, and acts like a true assistant. From querying data to automating workflows — MCP is the secret sauce to making AI smarter and more useful in the real world. 👉 Check out my blog and see how MCP can transform chatbots into AI agents that think + act: https://lnkd.in/gZA9EuHQ #ArtificialIntelligence #ConversationalAI #AIAgents #FutureOfWork

If your MCP just wraps existing APIs, it’s time to rethink. MCPs should be built for LLMs—resolve ambiguity, handle name matching, and return what the AI really needs. Don’t force multi-step calls or vague errors. One intent, one call—build smarter, not harder. #MCP #LLM #AI #ProductDesign #DevEx

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?

An annoying side effect of AI is that people now feel the need to submit 10 pages instead of a 2 ppt slides with a clear visualized framework. I never thought I'd say this...but can we bring back the ppt slides?