Practical guides on using AI tools like ChatGPT, GitHub Copilot in your workflow
96 articles
ChatGPT can generate structured logging configs fast, but without the right prompts, you end up with fields that shift between services, missing required keys, and parsers that break in production. Here's how to fix that.
ChatGPT can generate connection pool configs that look right but silently bleed connections under load. Learn exactly how to prompt it for production-safe settings that won't exhaust your database.
ChatGPT can stub out feature flag logic fast, but it almost always misses cache invalidation, stale reads, and runtime refresh patterns. Here's how to prompt it correctly and catch the bugs before they ship.
ChatGPT can scaffold a background job scheduler in minutes, but the default output often ignores locking, duplicate execution, and concurrency edge cases. Here's how to prompt for code that actually holds up in production.
ChatGPT can draft Dockerfile multi-stage builds quickly, but it often ships dev dependencies, root users, and massive base images into production. Here's how to prompt it accurately and audit what it gives you.
ChatGPT can sketch pagination logic in seconds, but it consistently stumbles on off-by-one errors, incorrect total-page calculations, and edge cases like empty result sets. Here's how to prompt your way to correct, production-ready pagination every time.
ChatGPT can draft Playwright tests fast, but the selectors it picks often break the moment your UI shifts. Here's how to prompt it for stable, maintainable test scripts that actually pass in CI.
Large Language Models don't distribute attention evenly across a prompt. Researchers have discovered that certain early tokens can act as "attention sinks," attracting disproportionate attention and influencing outputs in unexpected ways. Learn how attention sink tokens work, why they affect model
ChatGPT can draft retry logic fast, but the default output often misses jitter, ignores maximum attempt caps, or quietly creates infinite loops. Here's how to prompt it correctly and validate what it gives you.
ChatGPT can scaffold Celery configs fast, but it often misses retry logic, acks_late settings, and visibility timeout traps that let tasks disappear without a trace. Here's how to prompt your way to configs that actually hold up in production.
Scikit-learn pipelines are excellent for preventing data leakage and simplifying preprocessing, but they can sometimes transform your target variable (y) in unexpected ways. Learn why this happens, how it affects model performance, and how to prevent subtle training and evaluation bugs.
ChatGPT can draft gRPC proto files fast, but it routinely gets field types, service method signatures, and streaming patterns wrong. Here's how to prompt it correctly and catch the mistakes before they hit your build.