Course Roadmap

16 sections87 chapters

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#	Section / Chapter	Difficulty
1	Welcome
	Course Roadmap
	Join the Community
2	Introduction to AI Engineering
	What is AI Engineering?
	The Three Layers of the AI Stack
	The Modern AI Landscape
3	Python for AI
	Python Essentials for AI
	Functions, Decorators, and Generators
	Object-Oriented Python and Dataclasses
	Type Hints and Pydantic
	Working with Files and Data
	Async Python
4	Your First AI Application
	Making your first LLM API call
	Understanding LLM Parameters
	Structured Output from LLMs
	Streaming and Conversation Management
5	How LLMs Work
	What are LLMs?
	Tokenization
	Transformer Architecture Simplified
	How LLMs Generate Text
	The LLM Training Pipeline
6	Prompt Engineering
	What is Prompt Engineering?
	Anatomy of an Effective Prompt
	Advanced Prompting Techniques
	Context Engineering
	Prompt Templates and Pipelines
	Defending Against Prompt Injection
	Prompt Optimization and DSPy
7	Embeddings and Vector Search
	What are Embeddings?
	Choosing Embedding Models
	Vector Databases
	Building a Semantic Search Engine
	Scaling Vector Search
8	RAG and Retrieval
	What is RAG?
	Building a Production RAG Pipeline
	Advanced Retrieval Techniques
	RAG with Citations and Grounding
	Evaluating RAG Systems
	Conversational RAG
	GraphRAG and Knowledge Graphs
	Multimodal RAG
	Agentic RAG
9	Function Calling and Tool Use
	From Text Generation to Action
	Designing Tool Schemas
	Multi-Tool Orchestration
	Structured Workflows with Tool Use
10	Model Context Protocol (MCP)
	What is MCP?
	Building MCP Clients
	Remote MCP Servers
	Production MCP Server Patterns
11	Building AI Agents
	What are AI Agents?
	Agent Memory Systems
	Multi-Agent Systems
	Agent Frameworks
	Agent Reliability and Debugging
	Agent Evaluation and Testing
	Human-in-the-Loop Patterns
	Building AI Workflows
12	AI System Architecture
	Architecture Patterns for AI Applications
	Designing for Reliability
	Data Architecture for AI
	Scaling AI Applications
	The AI Gateway Pattern
	Building AI-Powered UX
	Caching Strategies for LLM Applications
	Rate Limiting and API Management
13	Production Deployment
	Containerizing AI Applications
	CI/CD for AI Applications
	Monitoring and Observability
	Handling Production Incidents
	Iterating in Production
	The AI Engineering Lifecycle
14	LLM Optimization
	Understanding AI Application Costs
	Prompt Optimization
	Model Selection and Routing
	Latency Optimization
	Self-Hosting Models
15	Agentic AI
	Computer Use Agents
	Coding Agents
	Deep Research Agents
	Agent Protocols
	Long-Running and Async Agents
	Agent Sandboxing and Security
16	Multimodal and Generative AI
	Vision Models and Image Understanding
	Text-to-Image Generation
	Audio and Speech Generation
	Video Understanding and Generation
	Building Multimodal Applications

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