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All capabilities · 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 hoursbeginner
Explore 3 tools for this project
Market relevance

Which roles ask for this — and how often

Share of job postings in India, per role, that name this capability.

Forward Deployed Engineer (AI)14%
What employers mean

You should be able to…

  1. Write a clear, scoped prompt/spec before letting an AI assistant generate code
  2. Review every AI-generated diff line by line before committing, not just accept-all
  3. Catch when an assistant hallucinates an API, library version or config that doesn't exist
  4. Use an assistant to write tests for existing code, then verify the tests actually fail on a bug
  5. Break a large feature into small, reviewable AI-assisted commits instead of one giant diff
  6. 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
Tools for practice

Choose a tool for the job

Start with one tool for each part of your project. You don’t need to learn them all.

Go to the practice brief

3 tools to explore

GitHub Copilot

Code

Explore coding assistance while checking suggestions against tests and your requirements.

Visit toolGetting started

Claude Code

Code

Work through coding tasks with an agent and inspect its changes before accepting them.

Visit toolGetting started

Practices & references

  • Code review checklists
  • Regression tests

Selected for practice by RoleGPS. No paid placements or affiliate links.
Access can change; check the details before starting.

Think your tool is a good fit? Let us know

Practice

AI-assisted refactor with a paper trail

Take a messy script of your own — an old college or hackathon project is ideal — and use an AI coding assistant to refactor it into typed, tested modules over four small PRs. Pin the current behaviour with characterisation tests before you change anything, so you can tell a refactor from a regression. For each PR write down what you asked for, what the assistant got wrong or invented, and what you changed before merging.

Start from

A messy script of your own — 200+ lines, no tests, no type hints (an old college or hackathon project works)

Milestones
  1. Pin current behaviour with characterisation tests and record baseline coverage · ~1.5h
  2. Two assistant-driven PRs: extract modules, then add type hints · ~1.5h
  3. Two more PRs for tests and error handling, each reviewed before merge · ~1h
  4. Write NOTES.md on what the assistant got wrong and re-measure coverage · ~1h
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

  • NOTES.md quoting at least two assistant mistakes verbatim — the API it invented, and the line you replaced it with
  • Before and after coverage from the same command, with the characterisation tests that pinned behaviour first
  • Four PRs whose review comments are yours, written on assistant-generated diffs
  • The diff you rejected outright, with one sentence on why the human answer was smaller
Signal it

Refactored an untyped 200-line script into typed, tested modules with an AI coding assistant across four reviewed PRs — characterisation tests caught every regression and every invented API before merge.

Interview

Questions you'll get asked

  1. Tell me about a time an AI coding assistant generated something wrong — how did you catch it?
  2. How do you decide when to write code yourself vs delegate it to an assistant?
  3. What's your process for reviewing a large AI-generated diff before merging?
  4. How would you prompt an assistant to refactor a function without changing its behavior?
  5. How do you verify AI-generated tests are actually testing the right thing?
  6. What guardrails would you put in place before letting an agent run shell commands in CI?
See where you stand for Forward Deployed Engineer (AI)