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.
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.
