Microsoft dropped a free 18-part Generative AI course, and it’s stacked.👇 Whether you're new to GenAI or brushing up for your next build, this i s a clear, technical walk-through from fundamentals to production-ready apps, all delivered by Microsoft Cloud Advocates. 🔍 Here’s what stood out: 1. How to compare LLMs (beyond just OpenAI vs Anthropic) 2. Prompt engineering → from basics to advanced techniques 3. Building real apps: chatbots, text generation, image generation 4. RAG, vector DBs, agents, fine-tuning — covered. 💡Bonus: UX, security, function calling, and low-code tools - Designed like a developer bootcamp. No fluff, no filler. Just 10–20 min videos with GitHub code to go deeper. - Ideal for devs, PMs, data scientists, and AI tinkerers This is the clearest, most actionable beginner GenAI series from a major cloud player to date. 👉 Check it out here: https://hubs.ly/Q03Jgrh-0 What’s the one topic in GenAI you still feel isn’t being explained clearly enough, even by the pros? Stay ahead with GenAI breakthroughs and product drops. Subscribe: https://hubs.ly/Q03Jgt-M0 #microsoft #aicourses #generativeai #freecourse #llm #machinelearning
Love that they went beyond just “hello world” demos. Covering RAG, agents, and even UX/security makes it much more production-focused than most free courses.
Love the practicality of this series. The one gap I still notice in most courses: governance and safety in applied GenAI. Everyone talks about building chatbots, but few really explain how to enforce compliance, manage data privacy, or mitigate hallucinations at scale. That’s the missing link between tinkering and enterprise deployment.
This looks like a great resource love that it goes beyond just prompting and actually dives into RAG, agents, and security. What’s often missing in most courses is the real-world application layer, how teams integrate these concepts into workflows to actually drive customer experience and growth. That’s where we’ve seen AI make the biggest impact.
Absolutely agree that Generative AI and LLMs are incredibly powerful — and right now they’re getting all the hype. But AI is not only about GenAI. Like any tool, they need to be applied to the right use cases. To truly bring AI into the core of a business, what’s needed is a hybrid and multimodal approach: combining the right “pieces” of AI for the right purposes. At AIThera, we believe the missing link is reasoning — enabling AI systems to go beyond outputs and become truly cognitive: capable of orchestrating different AI components, understanding context, explaining outcomes, and supporting real decision-making. That’s when AI moves from impressive demos to real business impact.
Great resource! I’d love to see more focus on AI orchestration, how multiple agents work together across enterprise workflows. That’s where the real ROI shows up.
This looks fantastic. What We’d love to see next is more depth on evaluating model outputs, not just building apps, but ensuring the outputs are reliable, explainable, and business-ready.
This course sounds fantastic and very comprehensive, perfect for deepening my understanding! How do you see prompt engineering evolving with new LLMs? Your updates shine, shall we connect?
This course sounds like the GenAI starter pack we’ve all been wishing dropped from the sky—Microsoft even remembered the low-code crowd this time! If only their next video tackled “How to debug a chatbot at 2am without caffeine.” One topic that always needs more love? Real integration and deployment, especially when combining multi-modal inputs or syncing real-time data. Platforms like https://www.chat-data.com/ help bridge the gap, making it a breeze to embed production-ready chatbots, connect APIs, and manage security without the headache. For anyone who finished lesson 18, that’s the next level!
For those that have taken the course, does it give an objective view across all the models? Also, any insights on the backend models and how they are trained?
Love this! A comprehensive, no-fluff course is exactly what the community needs right now to bridge the gap between theory and application. The focus on building real-world applications is key. The future of AI isn't just about the models, but about the clever, scalable systems we build around them. I'd be curious to see how the course touches on ethical AI and governance, which are increasingly crucial for anyone building production-ready systems. A great topic for a future course perhaps?