AI-driven development
Build reliable AI features and use coding assistants with clear requirements, safe tools, and verifiable results.
Showing 13–20 of 20 articles
Use AI to explain unfamiliar code without trusting invented architecture
Use AI to map unfamiliar code through concrete entry points, callers, and data flow, then verify its explanation without accepting invented architecture labels.
Write repository instructions that prevent repeated AI coding mistakes
Turn recurring AI coding review failures into short repository instructions with clear scope, concrete examples, and checks that remain useful as the project changes.
Keep AI assisted refactoring from becoming an accidental rewrite
Keep an AI assisted refactor behavior preserving by naming invariants, capturing representative outputs, and separating structural cleanup from product changes.
Make an AI coding task small enough to review and finish
Split AI coding work around observable outcomes and stable contracts so each change can be implemented, reviewed, and verified without expanding into a rewrite.
Use AI to reproduce a bug before asking it to fix anything
Turn an intermittent bug report into a controlled reproduction before using AI to propose a fix, then prove the correction changes the same observable failure.
Review an AI generated pull request for plausible but wrong code
Review AI generated code by tracing contracts, challenging assumptions, and reproducing failure paths that a passing build and plausible diff can easily miss.
Give an AI coding assistant enough repository context without overwhelming it
Build a focused context packet for an AI coding assistant using the real entry point, data contract, neighboring patterns, and checks needed for one repository change.
Turn a vague feature request into a testable engineering plan
Turn an ambiguous feature request into observable behavior, explicit constraints, and a small engineering plan an AI assistant can implement and you can verify.
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