Best Practices for AI Pair Programming

AI pair programming is a fundamentally different workflow than traditional coding. The AI moves fast, makes sweeping changes, and sometimes confidently breaks things. Learning to work effectively with AI means developing new habits and safety practices.

Here’s what we’ve learned from building mrq and watching how developers actually work with AI coding assistants.

Be Specific About Scope

Vague prompts are the number one cause of unexpected AI behavior. When you say “clean up this codebase,” you’re giving the AI permission to do almost anything. It might rename variables. It might delete files it considers redundant. It might restructure your entire project.

Instead, be explicit about what you want and what you don’t want:

“Refactor the fetchUser function in api/users.ts to use async/await. Don’t modify any other files.”

The more constraints you provide, the more predictable the output. It feels verbose at first, but it prevents the “I asked for one thing and got fifteen things I didn’t ask for” problem.

Work in Small Increments

When you ask an AI to build an entire feature at once, you get a massive diff that’s hard to review and impossible to partially revert. If something is wrong with step 3, you might have to redo steps 4 and 5 too.

Break work into small, reviewable chunks. “Create the data model” then “Add the API endpoint” then “Build the form component.” Each step is easy to verify. If something goes wrong, you know exactly where.

This feels slower, but it’s actually faster because you spend less time debugging cascading issues.

Actually Read the Diffs

It’s tempting to accept AI suggestions without careful review, especially when they appear to work. But working code can still have problems: hardcoded credentials, deleted imports that will break later, subtle changes to files you didn’t expect to be modified.

Before accepting any change, check:

Are files being modified that shouldn’t be?
Is anything being deleted?
Are there hardcoded values that should be environment variables?
Does the scope of the change match what you asked for?

This takes thirty seconds and prevents hours of debugging.

Assume AI Will Break Things

This isn’t pessimism, it’s realism. AI coding assistants are trained on patterns, not understanding. They will occasionally misinterpret your intent, hallucinate that code should be deleted, or make changes that break dependencies they can’t see.

You need a recovery strategy. The traditional approach is disciplined Git commits before every AI interaction. That works, but it’s friction that breaks your flow.

Alternatively, run mrq in the background. It captures every file change automatically, so when AI breaks something (and it will), you restore in seconds and keep working.

The point isn’t which approach you use, it’s that you have some approach. Coding with AI without a safety net is gambling with your work.

Keep Context Manageable

AI tools have context windows, and when you overload them, quality degrades. An AI with too much context starts making mistakes, misunderstanding references, and producing generic solutions.

Close files you’re not actively working on. Use .cursorignore or equivalent to exclude irrelevant directories. Start fresh conversations for new tasks rather than extending sessions indefinitely. Reference specific files rather than saying “the whole codebase.”

You’ll notice the AI gives better, more relevant responses when it’s not drowning in context.

Don’t Trust AI with Credentials

AI assistants will happily hardcode your API keys directly into source files. They’re optimizing for “works” not “secure.” If you ask it to connect to Stripe, expect something like:

const stripe = new Stripe("sk_live_abc123xyz");

Always check for hardcoded credentials before committing. Better yet, use tools that scan for exposed secrets automatically. mrq includes real-time security scanning that catches this before it reaches Git.

Know When to Take Over

AI excels at boilerplate, standard patterns, and repetitive code. It struggles with complex business logic, subtle bugs, and architectural decisions. Part of effective AI pair programming is recognizing when to stop prompting and start typing.

If you find yourself on the third or fourth attempt at getting the AI to understand what you want, it’s probably faster to write it yourself. The AI is a tool, not a replacement for knowing how to code.

Test Incrementally

After each AI change, run the code. Don’t stack five AI-generated changes and then discover the second one broke everything. Fast iteration requires fast feedback. Save, run, verify, then move on.

This is especially important because AI-generated code often works in isolation but fails when integrated. The sooner you catch integration issues, the easier they are to fix.

The Setup That Works

After working with thousands of developers building with AI, here’s the setup we recommend:

mrq running in the background for automatic protection
Git for intentional milestones when features are complete
Small, scoped prompts for predictable results
Review every diff before accepting
Test after every change for fast feedback

This gives you the speed of AI coding with the safety of traditional development. You can iterate as fast as the AI can generate, knowing you can always recover when things go wrong.

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mrq captures every file change while you code with AI. Automatic protection, security scanning, instant recovery.