Why MCP really is a big deal

What is Retrieval-Augmented Generation (RAG)?

Ready to become a certified GenAI engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdGhCF [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbGhPY0tlOE50SXhyVnNuWHlnZlA4YUdJX1hNZ3xBQ3Jtc0tsRVRvQVJnUW1BMmUybV91UnppcTYzWDNwelNDYjhVcVh3a1hsc1lNeHNIblJBSjZBdTNtU3VGRWVVcjdGS1pjVGlpVjlqOFpVeWRGenFXRURpczN0SllfV1ZKMWlKYXNFeFp4bk9nR2Y0WEVVZlFibw&q=https%3A%2F%2Fibm.biz%2FBdGhCF&v=T-D1OfcDW1M]Learn about the technology → https://ibm.biz/BdMsRT [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbTFYbWlYbWRfMTE3UjF5UWJRTzZVMlpIWXh1d3xBQ3Jtc0tsSW1NQU9iTTZnZ25qSjhpalhLb3NhdEtIV1hxMDBPeDF0cDBKNXVld09iVUE0QkF0VkRUelpQQldZTXhJNVVZNW9YTi1KMXR5ZnNnTUpwT2F6YWxJU0RueVFfb2pyWGkyRExVemkyd21FSDJWNXZZMA&q=https%3A%2F%2Fibm.biz%2FBdMsRT&v=T-D1OfcDW1M]Large language models usually give great answers, but because they're limited to the training data used to create the model. Over time they can become incomplete--or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation" or RAG. In this video, IBM Senior Research Scientist Marina Danilevsky explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates. Ref: https://www.youtube.com/watch?v=T-D1OfcDW1M

MCP vs API

MCP or API: Which transforms AI integration? Martin Keen explains how the Model Context Protocol (MCP) revolutionizes AI agents by enabling dynamic discovery, tool execution, and seamless external data retrieval. Discover how MCP simplifies LLM workflows and outpaces traditional APIs. Ref: https://www.youtube.com/watch?v=7j1t3UZA1TY

Why MCP really is a big deal

Tim Berglund is back at the lightboard with MCP (Model Context Protocol). MCP really is a big deal, but most people are missing the point. It's not just about enhancing desktop applications with agentic functionality—it's about exposing reusable tools and resources (including real-time data from sources like Apache Kafka) to build agentic microservices. Doing this takes us beyond basic LLM chatbots to dynamic, problem-solving systems that deliver real value in real professional settings.

Skills for the age of AI developer tools

With the rise of AI and automation, how do we as humans find our value in the workplace? How do we work with these new technologies? How do we build resilience to changes? What skills are needed for us to thrive in this new world?People have often felt apprehensive about change, especially big changes to the way we work. It happened in the industrial revolution and now it's happening with the rise of artificial intelligence. Five years ago these things existed, but now they are accessible to almost everyone. Ref: https://www.youtube.com/watch?v=IJjl4X1Lr8w

Devs want specs, Product Owners want speed

Learn how AI can change the game in an important scenario. The age-old battle between Product Owners and Developers rages on: POs push for speed, while devs demand clarity. When specs are too vague, developers waste time making assumptions. When specs are too detailed, POs get bogged down in documentation.The result? Context switching, frustration, and a backlog filled with half-baked work items.In this talk, Adam Cogan will show how AI powered tools like YakShaver (for GitHub and Azure DevOps) and recently Loom (for Jira) can act as the ultimate peacemaker.These tools capture discussions, structuring work items, and ensuring that every backlog item is ready… and assigned to the right backlog—all automatically. Ref: https://www.youtube.com/watch?v=u_JSpT3i1Z4&list=TLGGLRPrrPRtEmAyMDAzMjAyNg