(in Polish) Developing applications with LLMs and RAGs (Large Language Models and Retrieval-Augmented Generation) 1100-LLM-OG

The course focuses on:
- Fundamentals of Large Language Models (LLMs), their architectures, and applications.
- Concepts and implementation of Retrieval-Augmented Generation (RAG).
- Integration of retrieval systems with generative models.
- Efficient fine-tuning techniques for LLMs.
- Ethical considerations and biases in LLMs and RAGs.
- Use cases: summarization, Q&A systems, and chatbots.
- Practical demonstrations and hands-on exercises with popular frameworks and tools.

Type of course

general courses

Mode

Classroom

Prerequisites (description)

- Prior knowledge of Python programming and basic machine learning concepts is expected.

Course coordinators

Jan Kwapisz

Learning outcomes

Upon completing this course, students will:
1. Understand the architecture and working principles of LLMs.
2. Be able to explain the concept and implementation of RAG.
3. Analyze the ethical considerations in deploying LLMs and RAG systems.
4. Develop and deploy simple RAG-powered applications.
5. Apply efficient fine-tuning techniques to optimize LLMs for specific tasks.
6. Evaluate the performance and limitations of RAG systems.

Assessment criteria

- Regular attendance is mandatory.

- Submission of all assignments and projects.
- Active participation in workshops and group discussions.

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1100-LLM-OG in USOSweb