Damir
GenAI Unpacked: Beyond Basic
#1about 5 minutes
Controlling PowerShell with natural language commands
A live demo shows how a large language model can translate human language into executable PowerShell commands to manage system processes.
#2about 4 minutes
How large language models process text using tokens
Models break down text into numerical tokens for efficient processing, which is a fundamental concept for performance and cost calculation.
#3about 8 minutes
Calculating semantic similarity with embeddings and vectors
Text is converted into numerical vectors (embeddings) to calculate semantic similarity using cosine similarity, enabling features like recommendations and search.
#4about 5 minutes
How completion models generate text probabilistically
LLMs generate text by probabilistically selecting the next token, and the temperature parameter controls the creativity versus determinism of the output.
#5about 3 minutes
Implementing function calling with Semantic Kernel agents
An AI agent uses embeddings to understand user intent and trigger the correct external function or plugin from a library of available tools.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
01:00 MIN
Understanding the fundamentals of generative AI for developers
Java Meets AI: Empowering Spring Developers to Build Intelligent Apps
00:05 MIN
Moving beyond hype with real-world generative AI
Semantic AI: Why Embeddings Might Matter More Than LLMs
01:42 MIN
Understanding the fundamental shift to generative AI
Your Next AI Needs 10,000 GPUs. Now What?
18:03 MIN
GenAI applications and emerging professional roles
Enter the Brave New World of GenAI with Vector Search
23:35 MIN
Defining key GenAI concepts like GPT and LLMs
Enter the Brave New World of GenAI with Vector Search
14:43 MIN
Understanding the core components of a GenAI stack
Building Products in the era of GenAI
04:23 MIN
An overview of generative AI and its capabilities
Make it simple, using generative AI to accelerate learning
23:43 MIN
Key takeaways for building enterprise GenAI applications
Best practices: Building Enterprise Applications that leverage GenAI
Featured Partners
Related Videos
Best practices: Building Enterprise Applications that leverage GenAI
Damir
AI: Superhero or Supervillain? How and Why with Scott Hanselman
Scott Hanselman
Azure AI Foundry for Developers: Open Tools, Scalable Agents, Real Impact
Oliver Will
Develop AI-powered Applications with OpenAI Embeddings and Azure Search
Rainer Stropek
AI'll Be Back: Generative AI in Image, Video, and Audio Production
Fabian Pottbäcker, Thomas Endres & Martin Foertsch
Exploring LLMs across clouds
Tomislav Tipurić
Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure
Ricardo
Semantic AI: Why Embeddings Might Matter More Than LLMs
Christian Weyer
Related Articles
View all articles



From learning to earning
Jobs that call for the skills explored in this talk.


Front End Engineering Manager ( Generative AI experience )
Accenture
GraphQL
React Native
Continuous Integration


Product Owner Generative AI & NLP
Schwarz Dienstleistung KG

AI/ML Team Lead - Generative AI (LLMs, AWS)
Provectus
Remote
€96K
Senior
PyTorch
Tensorflow
Computer Vision
+2




AI Content Expert - English Speakers , Artificial General Intelligence
Amazon.com Inc.
HTML
JSON
Ruby
Data analysis