[ Index ]
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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 using AI coding assistants to write, review, and validate schema migrations — catching destructive operations before they reach production.
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 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 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.