[ Topic ]
Reliable prompting patterns for real production systems.
AI defaults to generic patterns from its training data. Here's how to anchor generated code to your existing conventions — with examples, explicit constraints, and a drift-detection review.
How to prompt AI coding assistants to produce diverse, realistic test fixtures and seed data — not just placeholder values that pass but don't catch real bugs.
AI coding assistants default to the happy path. Here's how to systematically prompt for input validation, error boundaries, and the failures that break production.
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.
Five battle-tested prompting patterns — structure, examples, decomposition, self-checking, and output contracts — that hold up when real traffic hits.