AI Vibe Coding & Security: ChatGPT, Cursor & TDD

Business users and developers are already pasting work into ChatGPT and AI coding tools. Without guardrails, that habit can expose PII, produce code nobody has checked, and make AI-built prototypes hard for engineers to trust or maintain.

Who to assign: This beginner-level course fits mixed teams. Project managers, analysts, and product owners learn to build small, useful apps safely. Developers and engineers learn to use Cursor for quick fixes without relearning whole languages. No programming background is required. The course covers HTML, CSS, and JavaScript basics in CodePen before moving into Cursor.

Risk and compliance framing: Learners practice writing prompts that keep sensitive data out of AI tools. They learn to sanitize AI-generated objects that hold emails or phone numbers, and to detect and anonymize PII with packages such as Presidio. They also learn how context-window limits and prompt injection affect model behavior. The course teaches Plan-Act-Check, a method of small, single-step agentic changes, and test-driven development practices. Both help AI-generated output get validated before it ships.

Time investment: About 5.4 hours across six skills. Most skills end with a hands-on challenge, finishing with a site deployed to GitHub Pages through Git.

Change-management note: The course pairs well with the AI Compliance, Privacy & Copyright courses and leads into AI Agentic Coding: ChatGPT, Cursor, Claude, and Copilot. That makes it a sensible first step in a team AI Playlist. Team Reporting lets Training Managers track who has completed the safe-use foundations before wider tool access is granted.

How this training helps your team succeed with AI-assisted coding

IT teams and business stakeholders are already pasting data into ChatGPT and accepting whatever an AI agent writes. This course replaces that habit with a repeatable, lower-risk workflow. Learners start with structured prompting in ChatGPT, then move into Cursor, and finish by pushing a working site to GitHub Pages using Git version control.

The course walks through practical AI-assisted coding risks and safeguards. Learners practice turning rough inputs into clearer AI prompts, managing context windows and token limits, sanitizing sensitive inputs, and recognizing prompt-injection risk. They also see why agentic coding needs structured planning, reviewable changes, and tests before AI-generated code moves forward.

• Fewer data-exposure incidents: Learners spot PII in AI-generated code and sanitize, tokenize, or anonymize it before sensitive data reaches a model.
• More predictable AI output: Context-Role-Expectation (CRE) prompting and zero-, one-, and few-shot examples help learners get usable results on the first try instead of after repeated retries.
• Safer agentic changes: The Plan-Act-Check method and lightweight test-driven development with assertions keep Cursor edits small, reviewable, and verifiable.
• Cleaner handoffs to engineering: Product owners and analysts learn Git staging, commits, and pushes, plus how to manage context windows and prompt-injection risk. Engineers receive traceable work they can trust instead of untracked snippets.

Knowledge

• How Software 1.0 (hand-written rules), 2.0 (models that learn from data), and 3.0 (plain-language prompting) differ, and why AI is more than ChatGPT
• The Context, Role, and Expectation (CRE) framework, plus zero-shot, one-shot, and few-shot prompting for steadier, paste-ready outputs
• How HTML provides structure, CSS adds design, and JavaScript adds interactivity, enough to read and question AI-generated code
• Why AI-generated objects can carry PII such as emails and phone numbers, and why that data must be sanitized or anonymized before it goes to a model
• How unclear prompts, weak constraints, and generative AI security risks such as prompt injection can lead to unreliable or unexpected outputs
• How Cursor’s agent, plan, and ask modes compare with a traditional VS Code workflow

Ability

• Write CRE-based prompts that produce consistent outputs from ChatGPT and agentic tools like Cursor
• Use the Plan-Act-Check (PAC) method to make small, single-step AI code changes that are easier to review
• Apply lightweight test-driven development with assertions to validate AI-generated code before accepting it
• Detect and anonymize sensitive data with redaction techniques or approved tools, then rebuild it when results come back from the model
• Use Cursor rules and focused agent workflows to keep prompts constrained and outputs reliable
• Version code with Git (stage, commit, push) and deploy an HTML, CSS, and JavaScript site to GitHub Pages so engineers can review and reproduce it