25 articles tagged #AI Coding Assistant
Modern AI coding assistants can do more than generate code—they can help debug it. Cursor AI's Agent Mode enables iterative debugging by inspecting code, proposing fixes, updating multiple files, and responding to compiler or test feedback. Learn how to use Agent Mode effectively while keeping human
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-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 produce regex patterns that look convincing but silently miss edge cases, match too broadly, or break on Unicode. Here's how to prompt your way to patterns you can actually trust in production.
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 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.
GitHub Copilot transformed AI-assisted coding, but it's not the only option for IntelliJ developers. Google's Gemini Code Assist introduces deeper conversational assistance, stronger context understanding, and tighter integration with Google's AI ecosystem. This guide compares Gemini Code Assist
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 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 scaffold logging middleware in seconds, but the default output often silences exceptions instead of surfacing them. Here's how to prompt your way to middleware that actually preserves error context.
ChatGPT can draft migration rollback scripts fast, but it often misses destructive-change traps, data-loss edge cases, and framework-specific conventions. Here's how to prompt it so the output is actually safe to run.