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Core Gen AI concepts including Transformer architecture, LLMs, attention mechanisms, tokens, embeddings, and foundational AI principles.

Python techniques and libraries used in Gen AI engineering: async programming, decorators, data processing, and AI-specific Python patterns.

Designing scalable Gen AI systems: RAG pipelines, multi-model architectures, latency optimization, and production system design.

Hands-on coding challenges for Gen AI engineers: implementing embeddings, building retrieval systems, and writing AI-powered tools.

Deploying and monitoring Gen AI models in production: MLOps, model serving, observability, cost optimization, and reliability.

Advanced Gen AI concepts: RLHF, constitutional AI, model alignment, interpretability, and cutting-edge research topics.

Extended Gen AI topics including vector databases, RAG, agents, MCP, fine-tuning, semantic search, AI safety, and emerging architectures.

Must-know concepts that cover foundational Gen AI engineering topics.

Google Agent Development Kit (ADK) - building, evaluating, and deploying AI agents with Google's open-source framework.

Tools and platforms for using, interacting with, and deploying AI models in production.