Programming foundations
Ship faster with AI coding assistants
Use Copilot/Cursor/Claude Code effectively: specify, review, test and not blindly trust generated code.
~6 focused hours·beginner
Tools: GitHub Copilot, Cursor, Claude Code, code review checklists
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Write a clear, scoped prompt/spec before letting an AI assistant generate code
- Review every AI-generated diff line by line before committing, not just accept-all
- Catch when an assistant hallucinates an API, library version or config that doesn't exist
- Use an assistant to write tests for existing code, then verify the tests actually fail on a bug
- Break a large feature into small, reviewable AI-assisted commits instead of one giant diff
- Explain in a review what an AI-generated PR does and why, in your own words
Needs first: Collaborate with Git and GitHub
Learn — free, link-checked
The few resources that matter
Read · beginner · 20 min · docs.github.com
Best practices for using GitHub Copilot
Official guidance on prompting, reviewing and not blindly trusting AI suggestions — the exact discipline interviewers probe for. — GitHub Docs
Read · beginner · 30 min · docs.claude.com
Claude Code overview
Shows how an agentic coding assistant plans, edits and runs code in your terminal — the newer skill employers now screen for alongside Copilot/Cursor. — Anthropic
Read · intermediate · 25 min · anthropic.com
Claude Code: Best practices for agentic coding
Anthropic engineers' own workflow (plan, verify, review diffs) for using an AI coding agent without blindly trusting its output. — Anthropic
Practice
AI-Assisted Refactor with a Paper Trail
Take an existing messy script or small repo (your own or a public one) and use an AI coding assistant to refactor it into typed, tested modules over a series of small PRs. For each PR, write a short note on what you asked for, what the assistant got wrong or hallucinated, and what you changed before merging.
Done when
- At least 4 PRs, each scoped to one change, each merged only after human review
- A NOTES.md log documenting at least 2 cases where you corrected AI-generated code
- Test coverage for the refactored modules did not regress (measured before/after)
- Final code has no leftover AI-invented APIs or config that don't actually exist
Prove it
Evidence a recruiter can check
- Public GitHub repo with the before/after code and PR history
- NOTES.md documenting AI mistakes caught and corrected during the refactor
- Before/after test coverage numbers showing the refactor didn't break anything
Interview
Questions you'll get asked
- Tell me about a time an AI coding assistant generated something wrong — how did you catch it?
- How do you decide when to write code yourself vs delegate it to an assistant?
- What's your process for reviewing a large AI-generated diff before merging?
- How would you prompt an assistant to refactor a function without changing its behavior?
- How do you verify AI-generated tests are actually testing the right thing?
- What guardrails would you put in place before letting an agent run shell commands in CI?