AI Agentic Coding: ChatGPT, Cursor, Claude, and Copilot

Developers are already pasting code into chatbots, and without shared guardrails that habit creates risk: leaked keys and secrets, exposed PII, and unreviewed AI-generated code reaching production. This course gives IT Directors and Team Leads a common, safety-first workflow for AI-assisted development across ChatGPT, GitHub Copilot, Cursor, and Claude Code.

Role fit: This course is built for developers, automation engineers, and QA testers who already code. The instructor states it is not entry-level. Team members without a programming background should start with the Vibe Coding & Security course. The AI Prompt Engineering course is an optional companion for anyone who wants to go deeper.

Time investment: The course runs about 4.9 hours across six skills of 44–54 minutes each. It is a mini-course, so it covers breadth rather than deep mastery of any single tool.

Risk and change management: Each skill reinforces a few core practices:

• Keep humans in the loop.
• Keep secrets and PII out of prompts.
• Send concise snippets rather than whole files.
• Review diffs before accepting changes.
• Verify with TDD or assertions.

The course is explicit that ChatGPT should not write production code. Real features belong in IDE-based agentic tools, where tests and history stay intact. The Plan-Act-Check loop lets your team adopt agents while keeping control over every step.

How CBT Nuggets helps: Each skill ends with a challenge to confirm there are no knowledge gaps. Experienced developers can skip ahead to a challenge to validate what they already know, which keeps seat time focused on what each person still needs.

How this training helps your team succeed in the real world

AI coding tools can speed up delivery, but they can also leak secrets, expose customer PII, or ship unreviewed code. IT teams complete this training to adopt ChatGPT, GitHub Copilot, Cursor, and Claude Code with guardrails already in place. Developers keep a human in the loop at every step instead of accepting whatever the model returns.

The course is built around realistic scenarios. In one, a developer is tempted to paste production code into a chat window. In another, an engineer lets an agent rewrite a large block of code without checking it. A third covers a team that wants to move from a written spec to a working website without losing control of the codebase.

• Fewer costly mistakes in production: Your team learns to keep production work in IDE-based agentic tools, where they can run tests and keep version history. ChatGPT is reserved for short exploratory examples.
• Safer handling of sensitive data: Practitioners learn to avoid sharing secrets and PII with AI tools. They send concise snippets instead of uploading entire files, which also keeps the model's context window focused.
• More reliable AI-generated code: Engineers apply the Plan-Act-Check loop. They review before-and-after diffs and verify changes with test-driven development (TDD) or assertions before anything is merged.
• Consistent prompting across the team: Developers use a shared approach to prompting. This includes Context-Role-Expectation (CRE), zero-shot to few-shot examples, and a six-step method for making one small change at a time. The result is predictable output instead of "garbage in, garbage out."

Knowledge

• How the software 1.0, 2.0, and 3.0 model frames traditional programming, machine learning, and plain-language programming with generative AI, and why human-in-the-loop guardrails apply to all three
• The CRE (Context, Role, Expectation) prompt pattern for code, plus when to escalate from zero-shot to one-shot or few-shot examples
• The six-step method for making one small, concrete change at a time, and why vague input produces vague output
• The Plan-Act-Check (PAC) loop, and how assistive workflows (GitHub Copilot) differ from agentic workflows (Cursor agent mode, Claude Code)
• Why ChatGPT suits exploring ideas and short examples, while production features belong in an IDE with agentic tools that run tests and keep history
• The risks of skipping software engineering principles, including exposed keys, secrets, and PII such as names, emails, addresses, and payment data

Ability

• Write tight, well-scoped prompts that steer AI coding tools toward predictable output
• Set up an IDE with a virtual environment and use GitHub Copilot for assistive coding
• Install Cursor, choose an appropriate plan, and run its agent mode through Plan, Act, Check, and Loop cycles to build a simple website
• Review diffs before and after each change, and validate AI-generated code with test-driven development (TDD) or assertions
• Keep the context window lean by sharing concise snippets instead of entire files, and keep secrets and PII out of prompts
• Coordinate AI agents as planners, coders, and testers while approving each step