Become an AI Engineer by building real-world agentic AI systems
InstructorNikolai SchulerThe curriculum includes hands-on paths for building specialized agents, such as calendar assistants, and implementing advanced retrieval techniques like RAG and CAG.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A node-based workflow diagram titled INVOICE PARSER - LLM showing triggers, processing steps, and an LLM chain.
A visual workflow diagram illustrating an AI Agent node connected to an OpenAI Chat Model, Simple Memory, and a Supabase Vector Store.
A schematic diagram illustrating the Cache-Augmented Generation process, showing how large data and user questions combine to generate an answer.
A screenshot of a visual workflow builder displaying connected nodes for LLM tasks such as sentiment analysis and text classification.
Instruction is suitable for non-technical individuals, providing clear objectives and a path to implement a functional agent early in the sequence.
Learners have noted that the N8N version used in lessons may differ from the current live version, causing interface mismatches. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The transition into practical exercises can be difficult without a dedicated section for account and table setup, and some advanced topics may feel rushed. These signals were recorded prior to the displayed update, and the update label does not prove a correction has been made.
Aligns well with beginners and non-technical professionals, though it may not meet the needs of those seeking broader engineering roles.
