Master GenAI Engineering —
The Complete Free Course

Animated layer-by-layer view of how a query travels through a production GenAI system — from UI to inference.

Visual breakdown of the three stages — Beginner, Intermediate, Senior — and the stack you own at each level.

Multi-layered architecture of a production GenAI app — from client request to LLM inference and back.

Side-by-side execution model: pipelines vs state machines — and when each breaks down in production.

ReAct, Plan-and-Execute, and Multi-Agent patterns with data flow diagrams.

Feature-by-feature comparison of AI code editors for production development.

Decision tree for when to retrieval-augment vs when to fine-tune — with trade-offs.

AWS Bedrock, Azure AI Foundry, Google Vertex AI — services and pricing side-by-side.

End-to-end retrieval pipeline: chunking, embedding, vector search, reranking, and generation — layer by layer.

From base model to deployed fine-tune: data prep, LoRA/QLoRA training, evaluation, and deployment flow.

Managed vs self-hosted vector databases — architecture, hybrid search, and cost trade-offs compared.