Turn Cursor from a code-completion tool into a disciplined, production-ready engineering system. This comprehensive hands-on course teaches you how to use AI across the full software delivery lifecycle: understanding requirements, planning architecture, implementing changes, debugging with evidence, testing, reviewing, releasing, and learning from real outcomes.
You will build a professional engineering workflow around Cursor, Git, GitHub, agents, cloud environments, automation, MCP tools, and repeatable verification. The course begins with the foundations of context, models, tools, runtime boundaries, and ownership. It then moves into repository navigation, rules and AGENTS md, task planning, architecture decisions, safe migrations, worktrees, parallel development, subagents, and reliable handoffs.
The projects go beyond toy prompts. You will work through realistic application scenarios involving APIs, databases, frontend flows, authorization, CI, pull requests, observability, incidents, and release recovery. You will also build and evaluate AI-powered product capabilities: ingestion pipelines, retrieval and RAG, source-grounded answers, runtime agent graphs, tool contracts, prompt-injection defenses, quality evaluation, latency, and cost measurement.
Every lesson is designed around observable evidence. You will learn to define acceptance conditions, inspect diffs, preserve existing behavior, choose the right Cursor mode, collect logs and test results, review agent output, and stop automation safely when the evidence is insufficient. Dedicated modules cover Cursor CLI and SDK workflows, Cloud Agents, self-hosted machines, team operations, GitHub review automation, and long-running engineering work.
