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Learn the idea behind AI search, recommendations, and "chat with your documents" tools. See how embeddings turn meaning into numbers, why close means related, how semantic search beats keyword search, where embeddings show up in everyday apps, why document search sometimes misses, and how to judge tools built on it. No code, no math.
2 módulos • 5 aulas
Go beyond wording a single prompt. Learn to control everything an AI model sees: build a reusable context pack, feed long documents so nothing gets lost, choose between pasting and retrieval, and manage long conversations with clean handoffs. Works with ChatGPT, Claude, Gemini, and any other assistant. No code.
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).
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.
Understand what really happens when an AI agent books, searches, codes, or browses for you. Learn how a chatbot becomes an agent, follow the agent loop step by step through a real trace, see how tools and connectors work, why agents forget, why they fail, and how to judge an agent product before you trust it. No code, no math.
Learn what physically happens when AI runs. See which parts of a computer matter for AI, why GPUs and AI chips took over, why memory is the real limit on model size and speed, how quantization shrinks models, and what goes on inside the data centers that train and serve them. No code, no math.
Some AI models pause to "think" before they answer. Learn what reasoning models actually do, why thinking step by step helps, how models are trained to reason, when a reasoning model is worth the extra time and cost, how to prompt one well, and where their thinking still goes wrong. No code, no math.