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Cursor AI Masterclass: Engineering Workflows & Methods
Hot & New
New
17 students
Cursor AI Masterclass: Engineering Workflows & Methods
Build, debug, test, review, and ship software with Cursor agents, GitHub, cloud workflows, RAG, and AI automation.
Created bySuperposition AI
Last updated 9/2026
English
English
What you'll learn
- Use Cursor AI to plan, implement, debug, test, review, and ship production-ready software through an evidence-driven workflow.
- Design reliable agent workflows with clear context, permissions, tool contracts, checkpoints, and human approval boundaries.
- Apply Git, GitHub, CI, worktrees, cloud agents, MCP, automation, and observability to realistic engineering projects.
- Build and evaluate AI product features including RAG, retrieval, agent state graphs, security tests, quality, latency, and cost.
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Course content
32 sections • 193 lectures • 31h 45m total length
Requirements
- Basic programming experience and familiarity with a code editor, terminal, and Git are helpful, but advanced AI experience is not required.
- You need a computer that can run Cursor, access to a practice repository, and willingness to execute commands and inspect results.
Description
This course contains the use of artificial intelligence.
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.
The course includes 32 structured modules, more than 31 hours of video, two substantial project tracks, a capstone workflow, English captions, and downloadable resources for every lesson. These resources include code, prompts, exercises, templates, decision records, test artifacts, and worked examples.
By the end, you will have a reusable professional workflow for shipping software with AI while keeping human judgment, security, maintainability, and verification at the center.
Who this course is for:
- Software developers and technical leads who want to use Cursor and AI agents professionally, from fundamentals through advanced team workflows.
- Engineers moving from ad-hoc prompting to repeatable, reviewable, and secure AI-assisted software delivery.
- Developers building AI-powered products with APIs, data pipelines, retrieval, RAG, evaluations, and runtime agent workflows.