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Use an AI Coding Assistant Without Exposing API Keys

Official VS Code example of reviewing an AI-suggested code change; the example code is not a project claimed by this publication.
Official VS Code example of reviewing an AI-suggested code change; the example code is not a project claimed by this publication. Image source. © Microsoft. Interface versions may differ.

A code assistant may need context, but it does not need your production secrets. Start a student or freelance project with a deliberate boundary between examples and live credentials.

A practical sequence

  1. Identify sensitive files before sharing the project: environment files, private keys, database exports and customer records. Replace secrets in examples with clearly fake placeholders.
  2. Keep server credentials on the server and use environment configuration appropriate to your host. A value embedded in browser JavaScript is visible to visitors.
  3. Review every suggested dependency and command before running it. Ask what data leaves the machine and what permissions a new integration needs.
  4. Inspect the final changes and the repository history for accidental secret inclusion. If a real credential has been exposed, revoke or rotate it through the provider rather than only deleting the visible line.

An example to adapt

Assistant instruction: Use placeholder environment variable names. Do not request, print or embed live credentials. Explain the trust boundary for any new service.

Try the example in a copy or a small project before applying it to live work. Keep the working version available and record the exact change you made. For a college project or a freelance handoff, being able to explain the decision and demonstrate the result is more valuable than adding features you cannot maintain.

What to check before you finish

Ignoring a file prevents future accidental commits; it does not erase an earlier committed secret. Treat repository visibility and credential recovery as separate checks.

Verify the intended behaviour as well as one failure case. Record what worked, what remains uncertain and how to reverse the change. An AI-generated explanation can help you investigate, but the running project and its actual output are the evidence.