The course is intended for participants who already have a basic understanding of algorithms and wish to deepen their knowledge of using Large Language Models (LLMs) and their APIs.
Familiarity with one of the programming languages (Python, TypeScript, JavaScript, C/C++, etc.) is recommended but not required.
Prior knowledge of LLMs, AI agents, and RAG (Retrieval-Augmented Generation) is desirable but not essential.
During the course, participants will learn:
a more detailed insight into the architecture of LLMs and GPT,
how to extend RAG (Retrieval-Augmented Generation) with the integration of Knowledge Graphs,
advanced process automation using AI agents and the Model Context Protocol (MCP).
In the practical part, we will combine RAG and Knowledge Graphs to develop an advanced AI agent composed of multiple autonomous agents.
Development will be carried out using the Python programming language.
Content
A deeper insight into the architecture of LLMs and GPT
An extended RAG (Retrieval-Augmented Generation) concept with the integration of Knowledge Graphs
Integration of RAG and Knowledge Graphs into a unified system
Advanced process automation using AI agents and the Model Context Protocol (MCP)
- Razvoj naprednega AI agenta, sestavljenega iz več avtonomnih agentov
- Uporaba programskega jezika Python pri razvoju narednih LLM rešitev
Recommended prior knowledge
Knowledge of one of the programming languages (Python, TypeScript, JavaScript, C/C++, etc.) is welcome but not required. Prior knowledge of LLMs, AI agents, and RAG is desirable but not essential.
Learning objectives
Understand the architecture of LLMs and GPT,
Learn how to extend RAG (Retrieval-Augmented Generation) using Knowledge Graphs,
Develop practical skills in integrating RAG and Knowledge Graphs into a unified system,
Master advanced automation approaches using AI agents and the Model Context Protocol (MCP),
Build an advanced AI agent composed of multiple autonomous agents,
Strengthen proficiency in the Python programming language.
Target group
For anyone who wants to understand LLM and GPT architecture and get acquainted with advanced approaches to using LLM APIs and AI agents.