AI for Shell Scripts: Getting Safe, Correct Bash from an LLM
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.
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Everything we know about building AI software that actually ships — written for engineers, no fluff.
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 identify independent workstreams, fan them out to multiple AI agent sessions simultaneously, and merge results without conflicts.
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 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.