Curriculum — The AI Course

01

Getting Started

What AI engineering is, the roles it covers, and the baseline skills you need before diving in.

02

LLM Fundamentals

How large language models actually work: transformers, tokens, training, inference, and their limits.

03

The Model Landscape

The major model providers, open-weight families, local runtimes, and how to choose (and pay for) a model.

04

Prompt Engineering Fundamentals

Writing effective prompts: clarity, context, examples, roles, output control, and the core prompting techniques.

05

Advanced Prompting & Context Engineering

Beyond single prompts: ensembles, self-consistency, automatic prompt engineering, and managing the context window deliberately.

06

Working with Model APIs

Calling models in real code: provider APIs and SDKs, streaming, sampling parameters, and cost-saving techniques.

07

Embeddings & Vector Databases

Turning text into vectors: embedding models, similarity search, and the vector stores that power semantic retrieval.

08

Retrieval-Augmented Generation (RAG)

Grounding model answers in your own data: chunking, retrieval, generation, and when RAG beats fine-tuning.

09

AI Agents

From single prompts to autonomous loops: what agents are, where they shine, and the core agent architectures.

10

Tools & Function Calling

Giving models the ability to act: tool definitions, provider function-calling APIs, and the common tool categories.

11

Agent Memory

How agents remember: short vs long-term memory, episodic vs semantic stores, and strategies for summarizing and forgetting.

12

Agent Frameworks & SDKs

Building agents with (or without) a framework: LangChain, LlamaIndex, LangGraph, CrewAI, and the provider agent SDKs.

13

Model Context Protocol (MCP)

The open standard for connecting models to tools and data: MCP architecture, and building your own servers and clients.

14

Multimodal AI

Beyond text: vision, image generation, video, and speech — and the APIs and frameworks that combine them.

15

Evaluation & Observability

Knowing whether your AI system works: metrics, testing strategies, eval frameworks, and production tracing.

16

Security, Safety & Ethics

Shipping AI responsibly: prompt injection, red teaming, sandboxing, privacy, moderation, bias, and ethics.

17

AI Coding Tools

The AI-powered development tools reshaping how software gets built — and how to work with them effectively.