Available Courses

Demystify the Transformer architecture — the engine behind every modern LLM. Understand the forward pass, attention mechanisms, generation strategies, and the architectural innovations that enable models like Llama 3.

Deep dive into tokenizer internals, cross-model comparison, embedding training, and the encoder/decoder split — for learners who want to go beyond the fundamentals.

Understand embeddings, semantic similarity, and vector databases — the foundation for RAG and semantic search.

Build RAG systems: chunking strategies, retrieval pipelines, prompt integration, and evaluation.

Master 11 advanced RAG strategies — from re-ranking and semantic chunking to knowledge graphs, agentic retrieval, and fine-tuned embeddings.

From the agentic loop to multi-agent orchestration — understand what AI agents are, how they call tools, the design patterns that shape them, and how to coordinate multiple agents.

Master parallel AI collaboration with agent teams. Learn to coordinate multiple Claude instances for complex debugging, code reviews, and cross-layer development.

Build real-time voice agents: audio pipelines, turn-taking, latency engineering, and the modern orchestration stack.

Learn the core MCP concepts, architecture, transports, and FastMCP development workflow.

Protect AI systems from adversarial attacks, implement input/output guardrails, and scope permissions for tool-using agents.

Build evaluation pipelines, production monitoring, and feedback loops that keep AI systems reliable and improving.

Go beyond prompting — adapt generative models to your domain using SFT, QLoRA, RLHF, and DPO.