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34 articles
How to use AI to plan and execute code migrations reliably — from class components to hooks, callbacks to async/await — without introducing silent regressions.
How to capture, trim, and present compiler errors and test failures to AI coding tools so they fix the right problem — not a plausible-sounding neighbor of it.
Practical patterns for using AI to write complex SQL — window functions, CTEs, EXPLAIN analysis, and index suggestions — without getting burned by hallucinated columns or the wrong dialect.
How to use AI coding assistants to systematically spot injection flaws, auth bypasses, and insecure defaults — with prompts that actually surface real problems.
How to use AI coding assistants to design, implement, test, and clean up feature flags — concrete prompts for each stage of the toggle lifecycle.
AI defaults to generic patterns from its training data. Here's how to anchor generated code to your existing conventions — with examples, explicit constraints, and a drift-detection review.
How to structure AI-assisted merge conflict resolution — what context to provide, which conflict types AI handles well, and how to verify the result.
Practical techniques for using AI coding assistants on legacy and brownfield codebases — giving enough context, preventing over-modernization, and validating changes that can't break.
How to feed failing tests directly to your AI coding assistant and use TDD as a feedback loop that keeps AI-generated code correct from the first run.
How to use AI to extract OpenAPI specs from existing routes, generate type-safe clients, and keep spec and implementation in sync — without manual YAML.
How to prompt AI for shell scripts that handle errors, edge cases, and environment differences — and how to validate the output before you trust it.
Most developers default to one model for everything. Here's a practical framework for matching each coding task to the right model — cutting costs without sacrificing quality.
How to prompt AI coding assistants to produce diverse, realistic test fixtures and seed data — not just placeholder values that pass but don't catch real bugs.
AI coding assistants default to the happy path. Here's how to systematically prompt for input validation, error boundaries, and the failures that break production.
A practical workflow for feeding production logs to an LLM to surface root causes, correlate errors, and cut mean-time-to-resolution — without leaking sensitive data.
A practical guide to sharing the right context with AI assistants without exposing API keys, credentials, and sensitive data that creates security risk.
How to write a project instruction file that reliably steers AI coding assistants toward your conventions — what to include, what to leave out, and how to test it works.
How to recognize, prevent, and recover from AI-generated code that confidently calls methods, packages, or APIs that don't exist.
How to prompt AI coding assistants to produce pure functions, injectable dependencies, and explicit interfaces — so the generated code is easy to test before you write a single assertion.
How sharing your TypeScript types before asking for implementations dramatically improves AI-generated code — and how to use the compiler as a fast verification loop.
How to get AI coding assistants to write documentation that accurately reflects your code — not plausible-sounding summaries that drift from reality.
How to structure AI-assisted performance work — what profiling data to share, how to frame bottlenecks, and how to validate that a suggested fix actually helps.
How to use AI coding assistants as a first-pass code reviewer — prompting for real feedback on code you wrote, interpreting what it finds, and fitting it into your workflow.
How to give an AI coding assistant exactly the architectural context it needs to write code that fits your project — without overwhelming it with noise.
How to split a complex feature into agent-sized sub-tasks that execute reliably — with concrete patterns and scope rules that prevent drift and rework.
How to use AI coding assistants to navigate major library upgrades — feeding changelogs, mapping the breaking change surface, and validating the result.
Practical techniques for recognizing when an AI coding agent has gone off track and redirecting it effectively — before it digs you deeper into the wrong solution.
How to use AI coding assistants as a guide when navigating code you didn't write — from getting an architectural overview to tracing execution paths.
How to use AI coding assistants for safe, incremental refactoring — scoping the work, writing safety tests, reviewing generated diffs, and avoiding common pitfalls.
How to extend your AI coding assistant with Model Context Protocol servers — connecting it to your database, APIs, and custom tools in about ten minutes.
How to prompt AI coding assistants to generate tests that actually catch bugs — edge cases, failure modes, and boundary conditions, not just happy-path boilerplate.
How to keep AI coding assistants accurate across a long feature branch — when to trim, reset, and summarize context so the model stays focused.
How to structure debugging sessions with an AI assistant — what runtime context to share, how to frame the problem, and how to verify a diagnosis before patching.
A practical checklist for reviewing AI-written code — catching hallucinated APIs, subtle logic bugs, and security gaps before they reach production.