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AI Career Skills Course
Module 5: AI For Coding
AI, Machine Learning and Generative AI Basics
LLMs, Tokens and Context Windows
Hallucinations, Confidence and AI Limitations
Clear Task Prompts and Output Formats
Context, Examples and Few-Shot Prompting
Prompt Debugging and Iteration
AI for Writing, Summaries and Emails
AI for Research and Meeting Notes
AI Automation for Routine Office Work
AI Study Planning and Concept Learning
AI Practice Questions and Feedback Loops
Using AI Responsibly in Assignments
AI for Code Explanation and Debugging
AI for Tests, Refactors and Code Review
AI Pair Programming Workflow
AI for Spreadsheet Cleanup and Analysis
Asking Better Data Questions
Chart, Insight and Decision Summaries
Embeddings and Semantic Search
RAG Workflows and Knowledge Bases
AI Agents, Tools and Automation
Privacy, Sensitive Data and Access Control
Bias, Fairness and Harmful Output Checks
Evaluating AI Answers Before Use
CONTENTS

AI for Tests, Refactors and Code Review

Ask for safer tests, smaller refactors, and actionable review comments.

AI Career Skills Course
Module 5: AI For Coding
AI career skills
generative AI
+7
May 28, 2026
27
A

Learning Outcome

Ask for safer tests, smaller refactors, and actionable review comments.

Core Ideas

  • Unit test: Small automated check of one behavior.
  • Regression test: Test that prevents an old bug from returning.
  • Refactor: Improve structure without changing behavior.
  • Review comment: Specific risk or improvement suggestion.

Career Use Case

An engineer can ask AI for missing edge cases and review comments while keeping final judgement with the code owner.

Practical Workflow

  1. Start by naming the outcome: what should improve after using AI for Tests, Refactors and Code Review?
  2. Add the input material, constraints, and success criteria before asking for output.
  3. Ask for assumptions and uncertainty when the answer affects a real decision.
  4. Verify important claims, numbers, and policy statements before publishing or acting.

Hands-On Mini Task

  • Ask AI to propose tests for a function, then mark which tests protect real behavior.
  • A good review is specific, reproducible, and tied to user-visible risk.
  • Before moving on, explain how Unit test and Regression test change the decision.

Common Mistakes

  • Using a generic prompt when the task needs clear context.
  • Accepting polished wording as proof of accuracy.
  • Sharing private data without redaction or approval.
  • Skipping a final human review for important decisions.

Quick Revision

Module 5: AI For Coding lesson 14 is about practical judgement: use AI to increase speed, but keep the goal, context, evidence, and accountability clear.

FAQs

Is AI for Tests, Refactors and Code Review only for technical users?

No. The course treats AI as a practical workplace and learning skill, with technical depth only where it improves judgement.

Should I trust AI output immediately?

No. Use AI to accelerate work, then verify facts, privacy, source fit, and reasoning before relying on the result.

What should I practice after this lesson?

Ask AI to propose tests for a function, then mark which tests protect real behavior.

How does the linked practice quiz help?

The practice quiz checks the lesson concepts immediately with feedback, while the paid mock bundle uses separate assessment questions.

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mediumAI Career Skills
Practice Quiz 14: AI for Tests, Refactors and Code Review
12 questions12 min

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Lesson 2 of 3 in Module 5: AI For Coding
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AI for Code Explanation and Debugging
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