Generative AI for Beginners

Topics covered

• •Set up your working environment and get API access from one of the providers
• •Understand what generative AI is and how the technology evolved historically
• •Get familiar with the inner workings of large language models (LLMs)

Topics covered

• •Understand what prompt engineering is and why it matters
• •Break down the components of a prompt and how tokenization affects the output
• •Master the basic practices of prompt construction (context, examples, templates)

Topics covered

• •Build your first text-generation application using an LLM API
• •Master model parameters: temperature, top_p, max tokens
• •Understand the specifics of chat applications: context, history, quality metrics

Topics covered

• •Get familiar with low-code/no-code development of AI applications (Power Platform, Copilot Studio)
• •Understand the function calling mechanism and its use cases
• •Learn to connect LLMs to external APIs and structure the model's responses

Topics covered

• •Learn about threats and risks facing AI systems: prompt injection, data poisoning, and data leaks
• •Master techniques for protecting LLM applications and their data
• •Understand the lifecycle of a generative AI application (LLMOps): from idea to production

Topics covered

• •Get familiar with the open-model ecosystem and the Hugging Face platform
• •Learn how to choose an open model and understand its license terms
• •Understand what AI agents are: state, tools, frameworks

Topics covered

• •Understand what small language models (SLMs) are and their advantages
• •Explore the Microsoft Phi family and SLM use cases
• •Get familiar with the Mistral model lineup and its distinguishing features