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15 articles
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 identify independent workstreams, fan them out to multiple AI agent sessions simultaneously, and merge results without conflicts.
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 give AI coding assistants the context they need to write GitHub Actions, Dockerfiles, and CI pipelines that work — not just pipelines that look like they work.
How to get AI coding assistants to write documentation that accurately reflects your code — not plausible-sounding summaries that drift from reality.
How to use AI to write commit messages, pull request descriptions, and changelogs that actually communicate intent — and where the workflow breaks down.
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 keep AI coding assistants accurate across a long feature branch — when to trim, reset, and summarize context so the model stays focused.