The d4-rich Prompt Pattern#

Non-reasoning chat models (deepseek-V4-Flash, grok-4.3, kimi with thinking disabled) collapse on multi-file refactor tasks when given thin or baseline prompts. Pass rates of 0-33% on canaries that reasoning models clear at 67-100%. The cheap fix is a three-part prompt addendum: completion checklist, callsites-exhaustively-updated rule, and verify-before-push instruction. Drop it into the system prompt of a non-reasoning model and the canaries go green. Drop it into a reasoning model and you pay 12× more for 0% quality improvement.

Prompt Engineering for Infrastructure Operations#

Infrastructure prompts differ from general-purpose prompts in one critical way: the output often drives real actions on real systems. A hallucinated filename in a creative writing task is harmless. A hallucinated resource name in a Kubernetes delete command causes an outage. Every prompt pattern here is designed with that asymmetry in mind – prioritizing correctness and safety over cleverness.

Structured Output for Infrastructure Data#

Infrastructure operations produce structured data: IP addresses, resource names, status codes, configuration values. Free-form text responses create parsing fragility. Force structured output from the start.