Cursor: Complete AI-Powered Software Development Workflow

Choose your technology stack, draft a product requirements document with AI input, and build a complete project with boilerplate, git, and AI-assisted features.

Learn how to create a user-centered product requirements document (PRD) or specification that guides AI-driven software development, outlining context, users, needs, core features, user flows, tech stack, and success metrics.

Break the project into small, incremental steps using a to-do list built from the prd, emphasizing agile workflow, testing, ci/cd, and security at every step.

Set up version control with git to safely manage AI-generated code, initialize the repository, rename the default branch to main, review changes, and commit small, specific updates with clear messages.

Bootstrap a new project with AI-generated boilerplate and set a fresh context for root-level files. Review dependencies, run the app in a built-in browser, and validate the foundation.

Learn to rollback to a working version when ai-generated code breaks an application. Practice undoing changes and reverting commits with Git to recover quickly.

Automate code quality with CI/CD, building, testing, and deploying automatically via GitHub Actions, and set up the first pipeline with automated tests.

Connect your local project to GitHub to back up code, enable collaboration, and CI/CD by creating a private repository and pushing the main branch.

Set up a GitHub actions pipeline that runs on every push, installs dependencies, lints code, type-checks TypeScript, and builds the project automatically, providing a hands-off, automated workflow foundation.

Choose a merge method like squash and merge to create a single commit and trigger the pipeline after a reviewed pull request, with branch protection enforcing PRs and approvals.

Explore how to better control cursor behavior by selecting models, writing rules for consistency, and providing context across chats and browser references to aid informed AI decisions.

Choose the right model for your task to maximize output quality and replicability, using strong reasoning models for planning and cheaper models for basics, and compare options via benchmarks.

Create and manage project rules to shape AI behavior, define git commit message guidelines, and control rule scope and application across projects, then test rules to ensure consistency.

Provide clear context to speed up work and reduce ambiguity by referencing files, folders, or specific lines, including drag-and-drop, directory references, terminal data, and documentation URLs.

The cursor browser, chromium-based, provides built-in developer tools, element referencing, logs, and screenshots, enabling seamless integration with AI context and easy in-browser application launch.

Deploy your app to production by moving from local debugging to a publicly hosted, optimized build on Netlify with automatic deployments via GitHub Actions and preview environments for pull requests.

Learn to perform a manual Netlify deployment and prepare automated GitHub actions by configuring Netlify site ID, authentication token, and repository secrets.

Automate deployment with GitHub Actions by deploying to production on main pushes and using Netlify preview environments for pull requests.

Close this course with thanks and clear next steps: mark every lecture complete to unlock your certificate, use the help page, and connect or leave a review on LinkedIn.