Note
The lab will be modified throughout the course.
Sync your cloned repo with the upstream from time to time.
You are on the back-end team for the Learning Management Service.
The API is deployed and secured. Now the team wants to prove it works with real tests, add a front-end so users can interact with the data, and establish the habit of using AI agents as development tools.
A senior engineer explains your next assignment:
- Redeploy the back-end and observe how requests flow from
Swagger UIthrough theAPIto the database.- Write unit and end-to-end tests, discover existing bugs, and fix them.
- Add a front-end to the system and modify it using an AI coding agent.
Important
Communicate through issues and PRs and deliver a working deployment.
Read the tasks and complete them by yourself.
When stuck or not sure, ask the AI coding agent.
Use appropriate prompts.
Remember: Use the LLM to enhance your understanding, not replace it.
By the end of this lab, you should be able to:
- Deploy a back-end service to a remote VM.
- Use browser developer tools to inspect
HTTPrequests. - Examine the request path from
Swagger UIthrough theAPIto the database. - Construct unit and end-to-end tests for boundary-value cases.
- Diagnose bugs from failing test output and apply fixes.
- Use an AI coding agent to generate and refine tests.
- Differentiate between a dev server and production static files.
- Use an AI coding agent to modify front-end code and observe the result.
In simple words, you should be able to say:
- I redeployed the system and observed requests flowing from Swagger to the API to the database!
- I wrote tests, found bugs, and fixed them!
- I added a front-end and modified it using an AI coding agent!
- Complete the lab setup