Free GenAI Course: Build a Real AI Agent from Scratch

LogicWiz's GenAI course is a free, hands-on generative AI course that teaches you to build a working AI agent in Python, not to watch someone else build one. All the lessons are free with no payment; the early chapters need no signup. Every lesson pairs written material with an in-browser lab and an AI tutor.

The Rise of Your AI Agent Empire — Build Nova — your own intelligent AI assistant — from first line of code to full agentic system.

What You'll Build in This Free GenAI Course

One project runs the length of the course. You build Nova, an AI assistant, lesson by lesson, and each module leaves you with something that runs:

A first Python script, written from scratch, with no prior Python assumed.
A prompted assistant that calls a real language model, once you know what a token and a context window cost you.
An agent loop: Nova receives a goal, picks an action, runs a tool, reads the result and decides again.
Retrieval over a private archive, built the long way, through embeddings, chunking, indexing and retrieval (RAG).
A team of agents orchestrated with LangGraph, rather than one model doing everything.
A voice interface, speech in and speech out, deployed behind an API.

Who This GenAI Course Is For

Complete beginners are the default reader: the course opens with Python from scratch (variables, data structures, conditionals, loops and functions) before it touches a language model, so no prior Python is assumed. It also suits self-taught engineers, students and working developers who can already code but have only used generative AI through a chat box and want to understand the agent loop, retrieval and orchestration underneath it.

Free GenAI 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.
MCP Explained With an Example, The Model Context Protocol in plain English, with the actual messages that go back and forth when an AI connects to a tool. Why it exists, what the handshake looks like, and the one mistake that breaks most MCP setups.
RAG Tutorial for Beginners: Build a Retrieval Pipeline in Python, What RAG actually is, explained in plain English, then built in about 40 lines of Python with no framework. Chunk, embed, retrieve, augment, generate, and the three places it quietly goes wrong.
RAG vs Fine-Tuning: Which One Should You Use?, A plain-English way to choose between RAG and fine-tuning. What each one actually changes, a three-question test, a worked example, and the mistake most teams make first.

What "hands-on" actually means in a free Generative AI course — a checklist for telling a genuinely hands-on AI course from one wearing the label.

Free GenAI Course Syllabus

Every lesson below is linked and free. The early chapters open without an account; for the later chapters each lesson has a public outline, and a free account (no card) opens the full lesson and its lab.

Awakening the Machine

Learn Python — the language of AI — and take your first steps toward building Nova

Building the Brain's Toolkit

Give Nova organized memory and the ability to make decisions

Leveling Up Your Powers

Build reusable abilities, unlock Python's ecosystem, and master data analysis

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

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.

Nova Goes to Production

Turn Nova's RAG prototype into a real product — orchestrate the whole pipeline with LangChain, hold a multi-turn conversation, measure answer quality, and let Nova reason with tools (agentic RAG).

Nova Builds a Team

Multi-agent systems: when one agent is not enough, the patterns that coordinate many (routing, orchestrator-worker, parallelization, sub-agents), and building it for real with LangGraph.

Nova Finds Her Voice

Conversational & multimodal AI: how models generate one token at a time, why tokens cost what they do, seeing and hearing (vision, music, audio), voice agents, natural conversation, and a voice-enabled multi-agent shopping assistant.

Nova Joins the Network

Give Nova reach beyond her frozen memory. Wire her to live tools, then tame the N×M wiring explosion with MCP — the Model Context Protocol: client/server, the list_tools/call_tool handshake, transports, gateways, and semantic tool-routing. Then go one level up to A2A, where agents discover and delegate tasks to each other, and learn to choose the right protocol for any job.

Finding the Needle: Hybrid Search & Retrieval

Nova powers the marketplace search now. Learn why keyword and semantic search each fail on their own, make dense search fast with ANN and HNSW, make sparse search smart with the inverted index and SPLADE, fuse both with Reciprocal Rank Fusion, and ship the whole hybrid pipeline on Qdrant — wrapped as a tool an agent can call.

Shipping Nova Safely: Observability, Guardrails, Evaluation & Cost

Nova's multi-agent support desk works in the demo - but a demo is not a production system. Learn the four layers that separate the two: observability (trace every step), evaluation (measure quality instead of guessing), guardrails (deterministic safety in and out), and cost control - then testing and bias checks that make Nova safe to ship.

Teaching FinAssistant the House Style: Fine-Tuning & Domain Adaptation

A careful prompt gets a model most of the way - but on a money desk, "most of the way" is a liability. This chapter is about the heaviest tool in the box: fine-tuning. When to reach for it (and when RAG or a better prompt wins), how LoRA and QLoRA teach new habits for a rounding error of the cost, how to build A-grade training data, and how to train, ship, and prove a fine-tuned model actually got better.

Operating TripWeaver: LLMOps in Production

A great prompt gets you a demo. Keeping an AI assistant good, safe, and affordable for thousands of real users is a different job entirely — LLMOps. This chapter is the operator's playbook: why AI fails silently, how to ship a change without betting the whole user base, versioning prompts like code, routing and retries, taming cost with caching, guardrails that keep it safe, and the observability that ties it all together.

Operating TripWeaver's Agent Team: AgentOps in Production

One assistant was hard enough to run. A team of agents — a Supervisor briefing Flights, Stays and Writer agents, with a Visa checker and a budget calculator on call — multiplies every cost, delay and failure. This chapter is the AgentOps playbook: when a team beats a single agent, the ways agents loop, misuse tools and compound errors (and the breakers that stop them), memory that stays useful without drowning the model, metrics and evaluations that measure the whole team, regression gates for prompts and tools, trust metrics like hallucination and PII leakage, and the rate limits, fallbacks and budgets that keep it all standing at scale.

Frequently Asked Questions About This Free GenAI Course

Is the LogicWiz GenAI course free?

Yes. All the lessons and their hands-on labs are free, with no payment. The early chapters (Python foundations) open without an account, so you can start building immediately; later chapters need a free account.

How much does the full GenAI course cost?

Nothing. Every lesson is free, and there is no upgrade needed to read or run any of them. LogicWiz separately offers paid practice plans, Ace at ₹149 per month and Wizard at ₹999 per month, for more test and interview practice alongside the course, but they buy no additional lessons.

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 this GenAI 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 GenAI course cover?

A growing library of lessons that covers 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.

Is there a free certificate for the GenAI course?

Yes. There is a free certification exam on LogicWiz Exams, and passing it gives you a certificate that anyone can check online. You do not need to pay for the course or the exam.

Do I need an OpenAI API key to do the labs?

Most labs that call a language model use your own OpenAI key or a free NVIDIA key, so the model provider bills you for those calls if you use OpenAI. The labs with several AI agents work without a key. The Python labs run in your browser, so there is nothing to install.

Who is the GenAI course for?

Students, career changers and developers who want to build AI agents, even if they have never written Python. If you already code, you can skip the early Python lessons and go straight to prompting, agents and retrieval.

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.