Practical guides on using AI tools like ChatGPT, GitHub Copilot in your workflow
96 articles
ChatGPT can sketch rate limit headers in seconds, but it routinely leaves out retry logic, resets timestamps, and client-side parsing — gaps that only show up when clients start hammering your API in production. Here's how to close them.
ChatGPT-generated queue consumer code looks clean until a malformed message traps your worker in an infinite retry loop and silently starves everything else. Here's how to prompt for the dead-letter, visibility-timeout, and backoff logic that actually makes consumers production-safe.
Your transformer model performs well after fine-tuning on a new dataset, but suddenly becomes much worse at the task it originally mastered. This phenomenon, known as catastrophic forgetting, is one of the biggest challenges in continual learning. Learn why it happens and practical strategies
ChatGPT can draft secrets rotation scripts in minutes, but the default output often creates dangerous downtime gaps where old credentials expire before services pick up the new ones. Here's how to prompt your way to safe, zero-downtime rotation logic.
If you're repeatedly telling GitHub Copilot about your coding style, project architecture, or preferred frameworks, you're wasting time. Custom Instructions allow you to provide persistent context so Copilot can generate more consistent, relevant code suggestions. Learn how to configure and use them
ChatGPT can scaffold RBAC middleware fast, but generic prompts produce generic code — and in access control, generic means gaps. Here's how to prompt your way to a production-ready implementation that won't leak permissions.
ChatGPT-generated seed scripts look clean until they hit your database and explode with foreign key errors, duplicate PKs, or null constraint failures. Here's how to prompt your way to seed data that actually inserts.
ChatGPT can draft Server-Sent Events handlers in minutes, but the output often silently drops events on reconnect or mishandles buffering. Here's how to prompt your way to production-grade SSE code.
Semantic caching can dramatically reduce LLM costs and response times, but many teams discover that seemingly identical questions still bypass the cache and trigger expensive model calls. Learn why semantic cache misses occur, how embedding similarity works, and how to build production-ready caching
ChatGPT can suggest useful database indexes, but it routinely misses compound index opportunities and column ordering rules. Here's how to prompt it correctly and catch the gaps before they hit production.
ChatGPT can generate memory leak detection scripts fast, but vague prompts produce noise-heavy tools that cry wolf on every GC pause. Here's how to prompt for precision and get scripts that catch real leaks.
ChatGPT's default CORS configs reach for wildcards the moment the prompt gets vague — and wildcards in production are a real security problem. Here's how to prompt precisely and get configs you can actually ship.