
From Failure to Guardrail: Building Project Memory for AI Work
An API compatibility example shows how to choose a guardrail, verify it catches the failure, and review it…

An API compatibility example shows how to choose a guardrail, verify it catches the failure, and review it…

A security, performance, and compatibility review of one API change shows how to separate findings from fixes and…

A test passes alone but fails in the full CI suite. Pass its results, the relevant CI log,…

Which parts of your AI task need to wait for earlier results? Use that question to choose a…

A disabled checkout button can pass a test while duplicate orders remain possible. Start with the failure you…

A practical framework for deciding whether AI-assisted work is verifiable, how much evidence it needs, and when AI…

Generated code is an output. AI-assisted engineering is the owned process that frames the work, verifies the result,…

Learn when to partition work by comparable units—and when a perspective-based split is the better fit.

When an undetected weakness would matter, challenge the result—then let evidence decide whether to keep, modify, or reject…

See how multiple perspectives can examine the same problem independently before their findings are combined into one result.

A practical guide to explicit AI handoffs, the difference between delegation and chaining, and when the additional overhead…

Prompt chaining turns a task into separate LLM calls. See when those boundaries help—and when one structured call…

Reusable workflow patterns help me structure AI-assisted work, coding, and non-coding tasks. The real value comes from combining…

A safe system migration requires more than a clean target architecture. Learn how small boundaries, controlled routing, transformation…