
Trilhas de aprendizado
AI Engineer
Go from Python basics to building real AI systems: understand how LLMs work, master prompting, then build RAG pipelines and AI agents step by step.
Selo da trilha
Conclua todos os cursos desta trilha para ganhar o selo.
Cursos desta trilha
- Etapa 1Iniciante
Interactive Python Practice
Learn Python programming with hands-on exercises. Practice variables, data types, functions, loops, and more with live code execution in your browser. No installation required - powered by Pyodide.
- Etapa 2Iniciante40 min
How LLMs Actually Work: Tokens, Parameters, and MoE Explained
Go one level under the hood of ChatGPT, Claude, and Gemini. Understand tokens, next-token prediction, context windows, model parameters, Mixture of Experts, and why AI models cost money to run. A short, no-code course for people who use AI every day.
- Etapa 3Iniciante~5h
Prompt Engineering Course: Master AI Prompts with Hands-On Practice
Master the art of crafting effective AI prompts through hands-on exercises. Learn prompt structure, few-shot learning, chain-of-thought reasoning, and advanced techniques with instant feedback. Build real-world prompts for code generation, content writing, and data analysis.
- Etapa 4Intermediário
Vector Databases: The Foundation of AI Apps
Master vector databases for AI applications. Learn embeddings, similarity search, and hands-on setup of Pinecone, pgvector, and Chroma. Understand indexing strategies, hybrid search, performance optimization, and how to choose the right database for your use case.
- Etapa 5Iniciante75 min
Local RAG for Beginners: Build a Private Knowledge Base
Build a private, offline knowledge base over your own notes and PDFs using a local AI model. A hands-on beginner micro course: run a model with Ollama, turn documents into embeddings, store them in a local Chroma database, and ask questions answered from your own files. No cloud APIs, no fees, your data stays on your machine. Want to deploy it as a web app instead? See the Full-Stack RAG course (/courses/fullstack-rag-nextjs-supabase-gemini).
- Etapa 6Iniciante35 min
Build Your First AI Agent in 30 Minutes
Build a working AI agent from scratch in Python. Learn the think-act-observe loop, call an LLM, give your agent a tool it can use, and handle a failed tool call. Framework-agnostic and beginner-friendly.
- Etapa 7Intermediário
Agentic AI with Python — LangChain & LangGraph
Build autonomous AI agents with Python using LangChain and LangGraph. Learn tool calling, stateful workflows, RAG-powered agents, multi-agent systems, and production deployment. From the ReACT pattern to a full customer support agent capstone.

