AI Pair Programming Best Practices

AI pair programming describes a workflow where a developer collaborates with a coding assistant during implementation or investigation. Depending on the product, it may suggest code inline, answer questions, summarize files or edit a workspace. Features differ by model, tool and repository permissions. The assistant can reduce blank-page effort, but it does not share the developer’s full understanding of product intent unless that context is supplied and verified. Begin with a bounded task. State the desired behavior, relevant files, constraints, edge cases and tests. For a bug, give a reproducible example and ask for a diagnosis before requesting a patch. For a new function, specify inputs, outputs, error behavior and security requirements. Ask for a small change, inspect the diff and keep feature changes separate from refactoring. If the task affects authentication, permissions, payments, personal data or production operations, use stricter review and involve an experienced developer. Treat suggestions as proposals. Confirm that code fits local conventions and dependencies, run tests, inspect error paths and review whether tests genuinely cover the requirement. Generated explanations can be wrong, and a passing test suite only checks what those tests cover. Do not paste secrets or private customer data into unapproved services. Limit repository or agent permissions to what the task needs, and review any action before it changes files, runs commands or connects to external services. Pairing also has a learning goal. Ask the assistant to explain alternatives, then restate the reasoning yourself. Keep track of which code you understand and which parts need follow-up. The cited review reports mixed outcomes and treats moderators from human-human pairing as research opportunities for human-AI pairing. Evaluate task complexity, expertise and interaction design in your workflow rather than assuming established universal effects. There is no universal productivity gain. Good practice optimizes for understandable, tested code and developer learning, not the volume of generated lines.