Practical guides on using AI tools like ChatGPT, GitHub Copilot in your workflow
102 articles
ChatGPT can draft distributed lock code fast, but split-brain bugs hide in the details — expiry races, unsafe release patterns, and missing fencing tokens. Learn the prompts that surface the real failure modes before they hit production.
ChatGPT can write retry code fast, but its default backoff strategies often miss jitter, cap limits, and context-aware delays — turning a resilience feature into a thundering herd. Here's how to prompt it correctly.
ChatGPT can generate mutex and locking code that looks correct at a glance but silently deadlocks under load. Learn the prompting techniques that force it to produce safe, production-ready concurrency code.
ChatGPT can draft SQL migrations fast, but the default output often contains ALTER TABLE statements that lock your production tables for minutes. Here's how to prompt it correctly and avoid downtime.
ChatGPT can scaffold DI containers fast, but it often gets the wiring wrong — circular deps, missing scopes, or services registered in the wrong lifetime. Here's how to prompt your way to configs that actually work.
ChatGPT can draft Elasticsearch queries fast, but without your actual index mapping it guesses field types and analyzers — and those guesses break at runtime. Here's how to get accurate queries every time.
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.