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.
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02:00 MIN
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01:06 MIN
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Understanding the fundamental shift to generative AI
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GenAI applications and emerging professional roles
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Defining key GenAI concepts like GPT and LLMs
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Understanding the core components of a GenAI stack
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An overview of generative AI and its capabilities
Make it simple, using generative AI to accelerate learning
00:41 MIN
Key takeaways for building enterprise GenAI applications
Best practices: Building Enterprise Applications that leverage GenAI
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From learning to earning
Jobs that call for the skills explored in this talk.



Commerz Direktservice GmbH
Duisburg, Germany
Intermediate
Senior

CGI Group Inc.
Köln, Germany
Senior
Data analysis
Natural Language Processing

Amdocs
Kontich, Belgium
Senior
Terraform
Kubernetes
Machine Learning
Continuous Integration


Infosupport
Veenendaal, Netherlands
€0K
Natural Language Processing

