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AI-Assisted Software Development

Use a coding assistant without losing control of your code. You give it a bounded task with clear evidence of done, read every line it changes, check the packages it suggests, and debug with tests instead of blind rewrites, on your own ticket-cleaner from the Python course.

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What you'll learn

By the end of this course, you will be able to:

  • Give coding assistants useful context and bounded tasks.
  • Inspect and test generated changes.
  • Debug systematically with AI support.
  • Maintain ownership of architecture, security and correctness.

About this course

Coding assistants can write a lot of code quickly. Some suggest the next line in your editor, some answer in a chat, and some change files and run commands on their own. The code they write often looks right. Sometimes it is right. Sometimes it changes things that you did not ask for, calls a package that does not exist, or passes the tests while it breaks what your users depend on.

In this course, you use an assistant on a project you already know: your ticket-cleaner from the Python course. Grace, the support lead at Larkfield, needs one new feature and has found one bug. Amira, a developer, shows how to do both safely, on a branch, with a pull request at the end.

First you prepare the project and decide what the assistant may touch. Then you write tasks with clear evidence of done, and you see how a vague request and a bounded request change the result. You review generated changes line by line, check every suggested package, and debug from a reproduced failure instead of asking the assistant to “fix it” again and again.

You do not need a coding assistant or any account. Every transcript in the lessons is from a real recorded session: the exact prompt, the assistant’s reply, the diff and the test results. A different run, or a different assistant, can give a different answer, so the lessons teach the habits that work with any of them.

Before you start

You completed Python for Practical AI Engineering and Git and Team Development Essentials, or you have the same skills. You do not need a coding assistant, an account or money. Every exercise comes with real recorded sessions of an assistant, so you can follow each step without one. If you have an assistant, you do the same tasks with it.

Courses to take first: Python for Practical AI Engineering, Git and Team Development Essentials.

Who this is for

People who finished Python for Practical AI Engineering and Git and Team Development Essentials, or have the same skills, and who want to use a coding assistant safely. Students, new developers and AI engineers who must stay the owner of their code.

Lead or use AI, but do not build it?

You can study the ideas of this course without writing code. Read modules 2 and 3 (about 2–3 hours), answer their knowledge checks, and get a free concepts certificate. You can come back for the practice later.

The paths for people who lead or use AI take this course this way. See the paths

How we made this course

Experienced AI practitioners designed this course. AI helped us draft the content; people chose the teaching approach and the examples, and reviewed every lesson. The outputs that you see are the actual results of running the code. How we make our courses

Inside the course

Module 4Debug and test with assistance

3 lessons

Debug with evidence. Reproduce Grace's bug with a minimal input, test one hypothesis at a time, and write a regression test that fails on the unfixed code.

  1. Reproduce first40 min
  2. Ask for evidence40 min
  3. Choose meaningful checks45 min

Final module · choose at least oneCase studies

2 case studies

Two end-to-end case studies on small projects that are new to you. Fix a bug in an unfamiliar codebase, or move a project across a real breaking change of a dependency. Complete at least one for the certificate; each one is a portfolio piece.

Complete at least one case study for the certificate. The others are optional. What each one covers

  1. Fix a bug in an unfamiliar codebase150 min
  2. Upgrade a dependency safely150 min

After the case studiesFinal assignment

Use every module in one practice project. You collect evidence, and check it against the course outcomes.

  1. Final assignment180 min

Case studies

Realistic projects from start to finish, on real data, with every output explained. Each one makes a portfolio piece. Complete at least one for the certificate; the others are optional.

  1. Fix a bug in an unfamiliar codebase2 hr 30 min

    Map a small Python project that you have never seen, reproduce a customer's bug, write the failing test, fix it with an assistant's help, and compare your fix with the maintainers' fix.

  2. Upgrade a dependency safely2 hr 30 min

    Move a small project across a real breaking change of a dependency, and check each suggested change against the changelog and the tests.

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