Practical guides on using AI tools like ChatGPT, GitHub Copilot in your workflow
101 articles
Data augmentation is designed to improve model generalization, but poorly chosen transformations can reduce accuracy, introduce label noise, and create unrealistic training examples. Learn why augmentation sometimes hurts performance and how to build augmentation pipelines that improve—not degrade
Modern AI coding assistants are evolving from autocomplete tools into autonomous coding partners capable of editing multiple files simultaneously. Windsurf AI's Cascade and Cursor's Composer both promise project-wide code generation, but they approach multi-file workflows differently.
ChatGPT can sketch circuit breaker logic in seconds, but the code it produces often flaps — tripping and recovering so fast the pattern becomes useless. Here's how to prompt your way to production-grade breaker logic.
LLM agents often perform well during the first few interactions but begin forgetting important details as conversations grow longer. This phenomenon—often called multi-turn memory collapse—is usually caused by context limitations, summarization strategies, retrieval failures, or prompt design
ChatGPT-generated idempotency key logic often looks correct but silently breaks under retries, race conditions, or partial failures — costing you real money. Here's how to prompt it properly.
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.