Generative AI Course — The Rise of Your AI Agent Empire
Build Nova — your own intelligent AI assistant — from first line of code to full agentic system
42 lessons across 10 modules. The first 4 lessons are free to start — no payment required.
Free tutorials
- Build an AI Agent From Scratch in Python — A working AI agent in ~60 lines of Python — no framework. The agent loop, tool calling, memory, and the failure modes nobody warns you about.
Syllabus
Awakening the Machine
Learn Python — the language of AI — and take your first steps toward building Nova
- Your First Words — free
- The Language of Intelligence — free
Building the Brain's Toolkit
Give Nova organized memory and the ability to make decisions
- Organizing Intelligence — free
- Teaching Machines to Decide — free
Leveling Up Your Powers
Build reusable abilities, unlock Python's ecosystem, and master data analysis
- Building Reusable Superpowers
- Unlocking the Arsenal
- Mastering Your Data
The Agent Awakens
Meet the machines behind Nova — from generative AI and LLMs to prompts, tools, memory, and the reflex-to-utility family of agents
- The Machine That Learned to Improvise
- Nova Finds Her Voice
- The Art of the Prompt
- The Two Walls
- The Agent Loop
- The Agent Family
Nova Reads the Archives
Retrieval-Augmented Generation — give Nova a searchable memory of LogicWizNews's private archive so she answers from real facts instead of guessing. Embeddings, semantic search, chunking, indexing, and retrieval.
- Why Nova Needs RAG
- Embeddings & Semantic Search
- Chunking: Breaking Down Long Documents
- Indexing & Retrieval
Nova at Scale (to be revised)
Cloud deployments, production RAG pipelines, enterprise security, and memory systems that persist across sessions.
- The Chunk That Knew Too Little
- Nova Goes to the Cloud
- Nova Never Forgets
Nova Orchestrates (to be revised)
Reflection loops, multi-agent coordination patterns, and live RAG pipelines that keep their own index fresh.
- The Story Nova Couldn’t Stop Polishing
- The Newsroom Swarm
- The News That Refused to Go Stale
Nova Under the Hood (to be revised)
Open the black box: language modeling foundations, tokenization, embeddings, attention, and how LLMs are pre-trained and fine-tuned.
- How Nova Actually Thinks
- Nova's Alphabet
- How Nova Pays Attention
- How Nova Learned Everything She Knows
- How Nova Learned to Be Nova
- Nova Sorts the Inbox
Nova Builds Her Crew (to be revised)
Graduate from one-shot chains to stateful, looping, multi-agent systems with LangGraph — the orchestration layer behind production AI agents. Build graphs, route control, run agents in parallel, give them real tools and memory, then ship them.
- When a Straight Line Isn't Enough
- The Shared Notebook
- Whose Turn Is It?
- Many Hands at Once
- Giving Nova Real Tools
- A Crew, Not a Soloist
- Nova Remembers
- Shipping the Crew
Nova Finds Her Voice (to be revised)
Nova can read and type — now give her ears and a mouth. Build the speech-to-text → LLM → text-to-speech voice pipeline, learn how machines turn sound into tokens, defend against voice cloning and prompt injection, put a validator agent in front of every action, and ship the whole thing as a fast, reliable, production-grade voice API.
- The Day Nova Learned to Listen
- How Nova Hears
- When a Voice Can Lie
- Nova's Bouncer
- Shipping Nova's Voice
Frequently asked questions
Is the LogicWiz Generative AI course free?
The first 4 lessons are free and need no signup — you can open them and start reading immediately. The remaining lessons require an account. See the pricing page for current access terms.
Do I need to know Python before starting?
No. The course opens with Python from scratch — variables, data structures, conditionals, loops and functions — before it touches language models, so a complete beginner can follow it end to end.
What do you actually build in the course?
You build Nova, an AI assistant, across the whole course: starting as a first Python script, gaining prompting and an agent loop, then retrieval over a private archive, then multi-agent orchestration with LangGraph, and finally a voice interface deployed as an API.
What topics does the course cover?
42 lessons across 10 modules: Python foundations, LLMs and prompt engineering, the agent loop and agent families, tool use, retrieval-augmented generation (embeddings, chunking, indexing, retrieval), LangGraph orchestration and multi-agent systems, transformer internals including tokenization and attention, and speech-to-text and text-to-speech pipelines.
What is the difference between an AI agent and a chatbot?
A chatbot completes one turn — you send text, it returns text, and control returns to your program. An agent runs a loop: it receives a goal, decides on an action, executes a tool, observes the result and decides again until the goal is met. The model, not your code, chooses the next step.
Are there hands-on exercises, or just reading?
Every lesson pairs written material with an in-browser lab containing practical exercises, plus an AI tutor you can ask questions mid-lesson.