Generative AI & LLM Applications with Python
SU-650(3 days)
Generative AI & LLM Applications with Python
15.600 DKK
(excl. VAT)
Course calendar
Description
Generative AI and Large Language Models (LLMs) can power much more than ordinary chatbots. Through APIs, you can integrate models directly into software and use them for text processing, analysis, automation, content generation and intelligent user interfaces, among other things.
In this course, you learn to work hands-on with LLMs from Python and build applications on top of existing models without having to train them yourself. You gain an understanding of how large language models work at the application level, how they differ from classic machine learning, and how tokens, context and model parameters affect the model's output.
You learn to write and structure instructions for the model, work with different types of prompts and control output for specific application scenarios. The focus is not just on getting a good answer in a chat, but on making model interaction reproducible and usable as part of software.
You also gain experience accessing models through APIs from providers such as OpenAI, Azure and Hugging Face, and handling model input and output programmatically. You also get an introduction to tool calling and see how a model can be connected to functions and external tools as a foundation for more advanced AI applications.
We also cover the limitations of the models and the responsible use of generative AI. Among other things, you learn about hallucinations and bias and learn to critically assess the quality of the model's responses.
The course gives you a practical developer foundation for moving on to more advanced LLM applications, where models are combined with your own data, external tools and workflows using techniques such as RAG and AI agents.
After the course, you can:
- Explain the key principles behind Generative AI and Large Language Models
- Structure prompts and instructions for specific application scenarios
- Work with LLMs through APIs from Python
- Control key model parameters and handle model input and output
- Integrate LLM functionality into your own Python applications
- Deal with hallucinations, bias and other limitations of generative AI
- Build toward more advanced LLM applications with RAG, tools and AI agents
Related exam
Prerequisites
To get the most out of the course, we recommend experience or knowledge equivalent to:
Next steps
If you want to build on your skills, these may be relevant:
Certification bundles
Module overview
- LLM stands for "Large Language Model" and is used when you want to generate new text based on existing text
- What is an LLM, and how does it differ from classic ML?
- Training vs. inference
- Model sizes, tokens and output control
- Prompt engineering is about writing questions and instructions that the LLM understands and responds well to
- Structure: system, user and assistant prompts
- Prompt strategies: few-shot, chain-of-thought
- Output formatting, control and testing
- An API is a kind of remote control for accessing an LLM over the internet
- Using the OpenAI, Azure and Hugging Face APIs
- Python SDKs and secure access
- Error handling and timeout strategies
- We look at how you can use LLMs in your own small Python programs or web apps
- Chatbot, generative writing assistant or coding assistant
- Integration with Streamlit, Flask or the terminal
- Adapting to user input and context
- AI can make mistakes and have built-in biases, and you need to learn to handle this responsibly
- Bias, hallucinations and responsible use
- Output limitations and controlling behavior
- Using role-based instructions
Free course advice
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