When AI Invents APIs: Handling Code Hallucinations in Practice
How to recognize, prevent, and recover from AI-generated code that confidently calls methods, packages, or APIs that don't exist.
[ Index ]
Page 02 / 04
How to recognize, prevent, and recover from AI-generated code that confidently calls methods, packages, or APIs that don't exist.
How to give AI coding assistants the context they need to write GitHub Actions, Dockerfiles, and CI pipelines that work — not just pipelines that look like they work.
How to prompt AI coding assistants to produce pure functions, injectable dependencies, and explicit interfaces — so the generated code is easy to test before you write a single assertion.
How sharing your TypeScript types before asking for implementations dramatically improves AI-generated code — and how to use the compiler as a fast verification loop.
How to get AI coding assistants to write documentation that accurately reflects your code — not plausible-sounding summaries that drift from reality.
How to structure AI-assisted performance work — what profiling data to share, how to frame bottlenecks, and how to validate that a suggested fix actually helps.
How to use AI to write commit messages, pull request descriptions, and changelogs that actually communicate intent — and where the workflow breaks down.
How to use AI coding assistants as a first-pass code reviewer — prompting for real feedback on code you wrote, interpreting what it finds, and fitting it into your workflow.
How to give an AI coding assistant exactly the architectural context it needs to write code that fits your project — without overwhelming it with noise.