I wrote a free 84-page handbook to take you from "what is a token" to working AI agents

For the past months, whenever someone asked me how to get into AI, I had no good single answer. The material out there is either academic theory, marketing hype, or fifty scattered tutorials that each assume you read the other forty-nine.

So I wrote the thing I wanted to hand over: Artificial Intelligence in Practice, an 84-page handbook that starts at zero and ends with you running agents with tools. It is free, and it just went up on GitHub:

https://github.com/vinimabreu/ai-in-practice

Who it is for

People starting out in AI, or curious about it, who want one coherent path instead of a pile of tabs. It assumes no AI background. If you can open a terminal, you can follow it. Developers already building with LLMs will find parts of it review, though the evaluation and cost chapters may still earn their time.

The path it walks

1. Foundations. What tokens, context windows and temperature actually are, and why a model "hallucinates". Setting up a real dev environment without pain.
2. Running models on your own machine. Ollama, picking a model family for your task, and honestly sizing what your hardware can run, including Apple Silicon.
3. RAG. Making an AI answer from YOUR documents instead of making things up, explained without hand-waving: embeddings, vector databases, chunking, and when RAG is the wrong answer.
4. Agents. The loop that turns a chatbot into something that does work: CrewAI, LangGraph, tools, browsing, MCP, and agents from different frameworks talking to each other.
5. Going deeper. Fine-tuning vs RAG, what the APIs really cost, security and privacy of autonomous agents, prompting as a method, and how to test AI systems so they do not embarrass you in front of a customer.
6. Three capstone projects, from a document Q&A assistant to a research agent crew, ending with what it takes to go from project to product.

Everything in it was tested by hand. Prices and version numbers were verified in mid-2026, and the book tells you to re-verify them, because this field does not sit still.

Why free

Because the version of me from a few years ago needed this and could not have paid for it. It is under CC BY-NC-SA: share it, translate it, use it to teach a course. Just keep it non-commercial and credit the source.

Two small asks

If you know someone who keeps saying "I want to learn AI but I do not know where to start", send them the link. That is exactly who it was written for.

And if it is useful to you, a star on the repo helps other people find it: https://github.com/vinimabreu/ai-in-practice

I will be serializing some of the deeper chapters here on dev.to over the next weeks, starting with the one I think is most neglected: how to actually test an LLM system. If there is a chapter you want first, tell me in the comments.

Vinicius Pereira