Seminar LLM: Developing AI-supported applications with Python
In-person events in Wiesbaden or online seminar on 2 days: €1,390 per person (net)
This two-day seminar aims to provide the essential skills needed to develop AI applications supported by Python LLM.
Dates for open training courses - compact course: 26/27 February 2025, 17/18 September 2025
LEARNING OBJECTIVES AND AGENDA
Goals :
Understanding LLMs : Understand the basics of Large Language Models (LLMs) and how they work.
Practical application : Learn the ability to effectively use online (e.g. ChatGPT) and offline (e.g. Ollama) LLMs in Python.
Prompt Engineering : Gain knowledge in creating and optimizing prompts for LLMs.
Integration of frameworks : Learn how to use Llama Index and Langchain to develop complex AI applications.
Zero-shot and Few-shot Techniques : Develop familiarity with zero-shot and few-shot learning methods.
Frontend Development : Learn how to create interactive user interfaces for LLM applications using Streamlit.
Practical implementation : Applying what you have learned in a realistic case study to gain practical experience.
Application Optimization : Understand strategies to optimize the performance and user experience of LLM-supported applications.
IN-HOUSE SEMINAR
Seminars held at the customer's location
€1,390.00
per day up to 4 participants plus statutory VAT
All content of the in-house seminars is individually tailored and taught to specific target groups .
Intensive follow-up support enables participants to implement their knowledge in the shortest possible time.
Recommended seminar duration: 2 days
Rental fees for training notebook (on request): 60,- Euro (per day, per training computer)
WORKSHOP
You tell us your topics!
Price on request
plus statutory VAT and travel expenses if applicable
All workshop content is individually tailored and taught to specific target groups .
We are happy to conduct the workshop at your location, in Wiesbaden or online.
Rental fees for training notebook (on request): 60,- Euro (per day, per training computer)
Day 1: Introduction to LLMs and basic implementations
Unit 1: Introduction to LLMs and Python
Overview of Large Language Models (LLMs) and their areas of application.
Comparison between online LLMs (e.g. ChatGPT) and offline LLMs (e.g. with Ollama).
Basic Python concepts for AI development.
Installation and setup of the required tools and libraries (e.g. transformers, torch).
Unit 2: Using LLMs with Python
Connection and use of online LLMs (e.g. ChatGPT API).
Introduction to offline use of LLMs with Ollama.
Hands-on: First interactions with LLMs in Python.
Example applications: text generation and simple dialogue systems.
Working with Llama Index and Langchain
Introduction to Llama Index and Langchain as frameworks for LLM-based applications.
Installation and first steps with Llama-Index.
Development of simple applications for data query and processing.
Use cases for Llama Index and Langchain.
Case Study
Application of what has been learned in a realistic case study.
Participants work in groups to develop an LLM-supported application.
Presentation of results and discussion.
Day 2: In-depth and advanced techniques
Unit 4: Advanced Techniques with LLMs
Introduction to techniques such as few-shot and zero-shot learning.
Using Langchain for complex workflow implementations.
Integration of LLMs into existing applications (e.g. chatbots, text analytics).
Unit 5: Optimization and Performance Management
Strategies for optimizing the performance of LLM applications.
Discussion of the challenges of using LLMs.
Hands-on: Implementing best practices to improve results.
Unit 6: Frontend Development with Streamlit
Introduction to Streamlit for developing interactive web applications.
Creation of user interfaces for LLM-supported applications.
Hands-on: Integrating LLMs into Streamlit applications for user-friendly presentations.
Case Study
Continuation of the case study from day one with a focus on advanced techniques.
Participants implement more complex functions in their applications.
Presentation of the final projects and feedback session.
CONTENTS
In today's digital era , Large Language Models (LLMs) are a crucial component of artificial intelligence (AI) and data analytics . Our two-day seminar, "Developing LLM-supported AI Applications with Python," offers a comprehensive introduction to the use and development of LLMs in Python. Participants will learn how to effectively use both online LLMs (such as ChatGPT ) and offline LLMs (e.g., with Ollama ). The goal is to deepen their knowledge of developing AI applications and acquire practical programming skills.
The seminar is divided into three 90-minute sessions and a case study. The first session covers the basic concepts of LLMs, including their functionality and areas of application. Through practical exercises, participants will learn how to integrate LLMs into Python to create simple text generation and dialog systems. Participants will gain practical access to the tools and libraries required for developing LLM-based applications.
In the second unit, we focus on advanced techniques such as zero-shot and few-shot learning. These methods are crucial for maximizing the flexibility and efficiency of LLMs in various use cases. Participants will be able to create complex workflows with Langchain and integrate LLMs into existing applications. We will also cover strategies for optimizing the performance of LLM applications to achieve the best results.
The second day of the seminar focuses on front-end development with Streamlit. Here, participants learn how to create interactive user interfaces that enable user-friendly presentations of their LLM-supported applications. Integrating LLMs into Streamlit applications offers the opportunity to develop innovative and user-centric solutions. The seminar concludes with a comprehensive case study in which participants can apply their acquired knowledge in a realistic project and present their results.
The implementation is always practical , and we're happy to use Python (Jupyter) based on your specific question (if you've booked the seminar exclusively as a corporate seminar). If you've booked a corporate seminar, you can use any IDE you like.
If you book a corporate seminar , we'll be happy to tailor the content to your needs and even provide training based on your data. Just contact us!