Master of Building AI Agents
Agentic Frameworks, RAG Systems, Knowledge Graphs, Orchestration, Chatbots
A comprehensive deep-dive into building intelligent AI agents. Learn everything from RAG fundamentals to knowledge graphs, from vector databases to multi-agent orchestration.
Get StartedDay by Day Breakdown
Each day builds on the previous, taking you from foundations to mastery.
RAG Fundamentals & Vector Systems
Master the foundations of retrieval-augmented generation.
What you'll learn
- What RAG is and why it reduces hallucinations
- Document chunking strategies and their tradeoffs
- How embeddings work and why they matter
- Vector databases (FAISS, Chroma) and similarity search
- Reranking techniques for better retrieval
- Building a complete vector RAG pipeline
What you'll practice
- Document extraction and preprocessing
- Comparing chunking strategies on real documents
- Building and querying a vector database
- Implementing a full vector RAG system
You leave with
- Understanding of when and why to use RAG
- A working vector RAG implementation
- Knowledge of retrieval quality tradeoffs
Knowledge Graphs & Advanced RAG
Go beyond vectors with structured knowledge and multi-hop reasoning.
What you'll learn
- Knowledge graphs as semantic layers
- Triplets (Subject, Predicate, Object) as knowledge units
- Graph-based RAG vs. vector-based RAG
- Multi-hop reasoning and query decomposition
- Self-prompting and iterative retrieval
- Combining graphs and vectors for hybrid systems
What you'll practice
- Building knowledge graphs with LLMs
- Triplet extraction and comparison
- Implementing graph-based RAG pipelines
- Multi-hop query resolution
You leave with
- Understanding of when graphs beat vectors
- A working knowledge graph RAG system
- Ability to design hybrid retrieval strategies
Agent Frameworks & Orchestration
Build intelligent agents that reason, act, and coordinate.
What you'll learn
- What makes an agent: Think → Act → Observe → Repeat
- Agent frameworks: LangChain, CrewAI, n8n
- Tool design and integration
- Multi-agent systems and coordination
- Evaluation with RAGAS framework
- Production considerations and best practices
What you'll practice
- Building agents with LangChain
- Creating workflows with n8n
- Designing and evaluating RAG agents
- Multi-agent coordination patterns
You leave with
- Ability to choose the right framework for your use case
- Working multi-agent system
- Knowledge of evaluation and improvement strategies
- Production-ready agent design patterns
Learn From the Experts
Sebastian Kessler
Senior AI Architect
German, English
13+ years of professional experience. Expert in production AI systems and multi-agent orchestration.
Adam Bilišič
Founder & AI-Augmented Coding Expert
English, Slovak
12+ years of professional experience. Ex-CTO of a Swiss company with 8+ years leading teams for enterprise clients. Expert in RAG systems and AI agent architectures.
Ready to Get Started?
Let's discuss how we can help transform your business with AI.