Overview

What if your IDE could read your repo, plan a refactor, and write the patch—while you stayed in control of every merge? This Short Course was created to help Software Development professionals master AI-assisted coding workflows with Cursor. By completing this course, you'll configure Cursor inside your IDE, craft scoped prompts that produce compilable refactors, and evaluate AI-generated pull requests for security, privacy, and secret exposure before approving them. By the end of this course, you will be able to: ● Configure Cursor within an existing development environment and connect it to the project repository ● Apply scoped prompt techniques to generate and insert refactored code that compiles successfully ● Evaluate AI-generated code changes for security, privacy, and secret-management compliance This course is unique because it treats AI coding as an engineering discipline—repository-aware, merge-ready, and secure—not just prompt tricks. To succeed, you should have intermediate software development experience, including Git and IDE fluency.

Syllabus

Configuring Cursor and Connecting to Your Repository

You will configure Cursor within an existing development environment and connect it to a Git repository to enable AI-assisted coding workflows. Topics covered include how Cursor integrates with VS Code and JetBrains IDEs, the role of repository indexing in improving AI suggestion quality; how to create and configure a .cursorignore file to exclude build artifacts, dependency directories, and credential files; and how to match AI model selection to task complexity. Through a vignette video, an instructional video, a reading, a screencast follow-along, two coach dialogues, a practice assignment, and a knowledge check quiz, you will build a fully configured Cursor environment with verified repository indexing. By the end of this module, you will be able to set up and validate a repository-aware Cursor environment ready for AI-assisted development.

Scoped Prompt Techniques for AI-Assisted Refactoring

You will apply scoped prompt techniques to generate AI-assisted refactored code that compiles successfully and is ready for peer review. Topics covered include the three pillars of scoped prompting — file selection, explicit constraints, and step-by-step change plans — how to distinguish modification targets from read-only context files, how to write verifiable constraints that protect function signatures and downstream dependencies, and how to sequence change plan steps so each produces an independently testable output. Through a vignette video, an instructional video, two readings, a coach dialogue, a practice assignment, and a knowledge check quiz, you will design and document a complete scoped prompt plan for a legacy module refactor. By the end of this module, you will be able to construct scoped prompts that produce bounded, reviewable AI-generated patches aligned with their team's codebase.

Evaluating AI-Generated Code for Security and Compliance

You will evaluate AI-generated code changes for security, privacy, and secret-management compliance before approving them for merge. Topics covered include why AI-generated code introduces specific security risks such as credential exposure, insecure defaults, and unsafe dependencies, how to apply a shift-left security review approach, and how to use OWASP Top 10 criteria, GitHub Secret Scanning, and the NIST SSDF as evaluation frameworks. Through an instructional video, two readings, a coach dialogue, a role play, a practice assignment, and a knowledge check quiz, you will apply a five-step evaluation procedure — scanning for secrets, applying OWASP criteria, assessing privacy implications, checking dependencies, and documenting findings — to a realistic AI-generated pull request. By the end of this module, you will be able to produce a written security assessment with a justified merge recommendation for any AI-generated contribution.