Solid AI and LLM foundations - You will structure your understanding of AI and Large Language Models, making it easier to judge what the technology can really do, separate hype from value, and choose solutions with confidence.
Smarter use of language models - You will understand how major LLM and SLM models work, when local models make sense, and how to approach multimodality and explainability so you can implement AI with fewer assumptions.
Chatbots grounded in your data - You will learn how to design GPT chatbots with RAG and vector databases, so they can answer based on your own knowledge sources instead of relying only on the model’s general knowledge.
Process automation in n8n - You will see how to build agents and workflows in n8n, connect multiple steps into one process, and move from a prompt to working automation that supports real day-to-day tasks.
Safer, compliant AI deployments - You will learn how to account for the AI Act, privacy, copyright, and Human-in-the-Loop checkpoints, helping you reduce risk and prepare AI solutions that fit organizational requirements.
Practical business use cases - You will explore concrete AI and LLM applications across industries, helping you spot processes worth automating, estimate business value faster, and plan pilots with clearer priorities.
Production-ready AI thinking - You will understand scaling, monitoring, and self-hosting of AI systems, so you can prepare solutions for real production use instead of stopping at prototypes, demos, or isolated tests.
Stronger AI project leadership - You will learn how to define team roles, measure project success, and oversee AI solution development, so you can collaborate more effectively with business teams, IT, and vendors.
Artificial intelligence is first framed through its definitions, historical development, and the main areas in which it is applied. This creates a shared reference point for later topics, while also outlining current trends and the directions shaping the future of AI.
Large Language Models are presented through their core concepts, leading model families, and practical role in natural language processing. The section also addresses multimodality, SLM and local-model security considerations, as well as explainability needed to interpret model behavior.
GPT chatbots combine model behavior, external knowledge, and data preparation into usable conversational systems. Participants examine chatbot structure, RAG architectures, vector databases for semantic retrieval, and the preparation of source data, with examples from business and education contexts.
Building autonomous workflows in n8n connects prompts, logic, and execution into one operating layer for agents. The section explains visual design of an agent’s “brain,” multi-agent orchestration, complete processes using n8n and MCP, and rapid prototyping through vibe-coding.
When LLMs enter business and public settings, regulatory and social risks become part of system design. This section covers AI Act compliance, copyright, privacy, data security, and Human-in-the-Loop checkpoints, then places these issues in the broader discussion about the future role of LLMs.
Advanced use cases show how AI and LLM move beyond basic assistants into domain-specific solutions across industries. The section also reviews current research directions and emerging development paths, helping relate today’s implementations to the next wave of AI capabilities.
Scaling LLM systems raises questions about infrastructure, operational control, and deployment architecture. Participants work through production management and monitoring practices, then examine self-hosting approaches for AI solutions where security, performance, or autonomy are key factors.
From an organizational perspective, successful AI delivery depends on clear ownership, stable operating models, and measurable outcomes. The section addresses how teams divide responsibilities around production models, how roles evolve in IT, and which success indicators matter in project governance.
Running AI initiatives in practice requires more than model selection alone. This part focuses on production monitoring, shifting responsibilities within IT teams, and the metrics used to assess whether an AI project delivers operational and business value over time.
The closing segment consolidates the concepts discussed throughout the course, linking foundations, implementation patterns, governance, and deployment choices into one coherent view. It also helps position LLM initiatives in terms of practical use, technical constraints, and organizational impact.
Comfort with computer work - You should be able to use a computer, a web browser, and online tools with ease, because the training involves AI platforms, digital materials, and basic service configuration.
Basic understanding of IT apps - You should understand how applications, network services, and data flows work across systems, so you can follow examples related to chatbots, integrations, and automation more easily.
Ability to analyze processes - You should be able to describe a simple business or learning process step by step, because the training shows how such processes are translated into AI use cases and automations.
Readiness for conceptual work - You should be ready to compare solutions, define requirements, and assess risk, because the training also covers architecture choices, compliance, and planning AI implementations.
