Imagines your system 12 months post-launch and catalogs what went wrong, covering data loss, cascading failures, performance cliffs, and operational overload.
Imagines your system 12 months post-launch and catalogs what went wrong, covering data loss, cascading failures, performance cliffs, and operational overload. Use it before finalizing a design to get failure scenarios ranked by likelihood times impact.
Run a pre-mortem on this proposed architecture. Assume it is 12 months from now and this system has failed in production in a significant way. What are the most likely causes? Cover: data loss scenarios, cascading failure paths, performance cliffs that only appear at scale, operational complexity that overwhelms the team, and failure modes that only surface under specific conditions (high concurrency, partial network failures, specific data shapes). Rank by likelihood × impact. Give me the top 5 only, with a one-sentence mitigation for each.
Source: https://dev.to/devprompts/10-claude-prompts-for-better-architecture-decisions-with-examples-12lg