Course Overview
This comprehensive hands-on course teaches how to use Cursor AI across the full software delivery lifecycle including planning, implementing, debugging, testing, reviewing, and shipping production-ready software. It covers building professional engineering workflows with Cursor agents, Git, GitHub, cloud workflows, automation, and AI-powered product features such as retrieval-augmented generation (RAG) and runtime agent graphs.
The course includes 32 modules and over 31 hours of video content, focusing on evidence-driven development, secure and maintainable AI-assisted software delivery, and advanced team workflows.
Key Takeaways
- 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, automation, and observability to realistic engineering projects.
- Build and evaluate AI product features including retrieval-augmented generation (RAG), retrieval, agent state graphs, security tests, quality, latency, and cost.
Prerequisites
- Basic programming experience and familiarity with a code editor, terminal, and Git are helpful but advanced AI experience is not required.
- A computer capable of running Cursor, access to a practice repository, and willingness to execute commands and inspect results.
Target Learners
- Software developers and technical leads wanting to use Cursor and AI agents professionally from fundamentals through advanced workflows.
- Engineers transitioning 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.
Course Introduction: What You'll Build 3:18
with evidence: the engineering workflow we will build 8:29
Model, context, tools, and runtime 11:00
Who owns the decision? 10:59
The workflow loop: understand, plan, build, verify, integrate 12:41
A small change with observable behavior 8:45
Entry assessment and learning paths 7:48
AgentBox, your computer, and a Cloud Agent machine 8:39
Install Cursor and record the environment version 7:53
Language tools, package managers, lockfiles, and bootstrap 8:35
Docker Compose, database health, and synthetic seed data 8:02
Working tree, staging, commits, branches, and diffs 9:04
Connect GitHub and verify a fresh clone 9:19
Command Palette, search, symbols, and platform shortcuts 8:06
Inspect, partially accept, and reject Tab suggestions 8:01
Bound an Inline Edit to the intended change 8:32
Choose direct editing, Inline Edit, or Agent 9:17
Inspect diffs and distinguish checkpoints from Git recovery 11:21
Repair a focused issue without rewriting the component 10:31
Navigate the editor, Agents Window, workspaces, and conversations 10:17
Choose Agent, Ask, Plan, or Debug for the problem 11:29
Model versus surrounding tools and instructions 8:50
Steer, queue, stop, and resume a conversation 9:09
Local execution modes, permissions, and sandbox behavior 8:05
Turn an editable plan into a verified change 8:01
Map entry points, services, tests, and ownership 10:31
Verify search and indexing coverage 8:44
Supply files, symbols, logs, images, and documentation 9:23
Understand ignore boundaries and tool access 7:32
Manage context and hand a task to a fresh conversation 8:01
Build a code map, domain glossary, and constraint package 8:21
Distinguish instructions, rules, skills, and shell commands 7:18
Write scoped project rules 8:22
Document repository commands and conventions in AGENTS.md 7:55
Build a skill for changing an API contract 8:41
Explicit invocation, discovery, and reusable modes 7:59
Remove duplicate guidance and repair a harmful rule 9:38
Product brief: user, problem, success and boundaries 10:44
Acceptance examples, invariants and quality constraints 11:42
Use planning to investigate code and alternatives 11:06
Record an architectural decision and its costs 10:17
Build a task DAG with dependencies and a critical path 11:55
Translate the plan into GitHub issues and reviewable PRs 11:46
Design a modular monolith and package boundaries 10:55
Model organizations, users, requests, approvals, and events 10:32
Create migrations, fixtures, and deterministic seed data 10:34
Define API validation and error contracts 10:02
Build request creation and a real status view 10:38
Verify a user journey and open the first feature PR 9:29
Separate authentication and authorization 10:10
Scope data access to the correct tenant 10:25
Enforce roles and valid approval transitions 13:51
Test contracts and unauthorized access cases 11:31
Audit transitions and make repeated actions safe 12:42
Change a permission rule and challenge the implementation 12:23
Create components and visual design tokens 12:20
Use visual selection and design context 8:59
Implement loading, empty, error, and optimistic states 12:31
Verify responsive behavior, keyboard use, and focus 11:19
Inspect console output, network requests, and results 11:52
Run Playwright and review visuals after a refactor 11:35
Access Files No pi required.
Free Course files 1 file package
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