AI-Assisted Scripting and Code Verification

Use an assistant to accelerate a small script without handing it the authority to define success. Write an explicit specification, predict results independently, inspect a flawed draft, build a reviewed replacement and prove that tests catch deliberate regressions. The outcome is a review portfolio you can use for future IT automation—not a promise that generated code is safe because it looks polished.

Use Python 3.10 or later and a new local practice directory. Python Foundations or equivalent familiarity with functions, dictionaries, exceptions and unittest is recommended. The examples use fictional tickets and no network. No paid AI service, real helpdesk export or administrator account is required. A supplied synthetic draft lets you complete the review exercises without sending anything to a model; it is not attributed to an actual vendor or model.

The script labels candidates for human review. It does not update priorities, close tickets, send messages, certify SLA compliance or access a ticket system. The supplied age values are fictional elapsed-hour measurements, not a business-hours calendar calculation. Operational use would require a separate collector, approved timing rules and an authorized action workflow.

Four chapters, with practical work

1. Define the decision before asking for code
2. Review the draft as executable behavior, not confident prose
3. Implement a reviewed replacement and reconcile its report
4. Prove your review and build a reusable workflow

Applied capability and learning route

Evaluate generated code through a specification, adversarial fixtures and observable postconditions.

Follow the lessons in order: establish the model, trace the supplied evidence, attempt the case, compare the worked solution, and complete the changed-condition project. The target is intermediate reasoning within this stated scope, not mastery of the entire profession or product. If a prerequisite is unfamiliar, pause and use the linked foundational teaching before attempting the project.

All teaching, explained answers and core synthetic practice are free. Some operational labs require separately available software, a licensed test tenant or an authorized lab; offline completion does not claim hands-on product proficiency.

How to complete this course

Study the worked examples, attempt the exercises before opening solutions, and submit your own project notes. Completion records are self-reported; practical competence requires the stated verification.