AcademyAtrask mokymusPractical development and implementation of Large Language Models (LLM)/AI workflows
10–11 December 2026
2 days, 16 academic hours
Online
EN
Course target
The training lasts 2 days and includes the opportunity to learn how to build intelligent AI workflows and assistants using large language models (LLM), as well as tools such as n8n and LangFlow.
There will also be an opportunity to learn more about RAG architecture, agent management (MCP, A2A), prompt engineering and the use of local models, as well as to work practically, for example, by creating bots and testing different models. The learning process uses examples that will then be applied in a real work environment.
Audience
- Developers
- DevOps and infrastructure specialists
- Automation engineers
- Data engineers
- Solution architects
- Anyone who wants to use AI tools and solutions in various projects and work processes.
By the end of the course, you will be able to
- Understand the structure of AI workflows, LLM and agent applications.
- Apply tools such as n8n, LangFlow and RAG models in practical projects.
- Analyze and evaluate the cost, performance and usability of LLM solutions.
- Create a practical AI workflow or assistant.
Prerequisites
- A general understanding of IT systems and an interest in automation and AI is desirable.
Course outline
- Overview of the AI tools ecosystem: LMM, vector databases, orchestration.
- Practical application of n8n and LangFlow tools for AI flows.
- Prompt design and the structure of test questions.
- Embedding and Retrieval-Augmented Generation (RAG).
- Agent control models: MCP, A2A.
- Comparison of LLM (OpenAI, Claude, Mistral, LLaMA etc).
- Local models and their deployment (price vs performance).
Certification exam
Not intended.
Lecturer
Andrejs Adamovičs
Upcoming training and schedule
Online
2026 m. gruodžio 10–11 d.09:00–16:15
Mokymai įgyvendinami LOGIN Academy partnerio Baltijas Datoru Akadēmija