The real danger isn't the coding; it's the review process. Laziness creeps into the PR stage, so I've implemented a "multi-model debate" system. I use Claude for 95% of the heavy lifting, then I hand the output to GPT for a cold audit. GPT acts like the pedantic nerd in the room, catching the gaps and inconsistencies that Claude tends to gloss over.
To keep quality high while I stay "lazy," I've built a massive documentation layer that actually outweighs the codebase. I treat my AI workflow like a deployment pipeline:
The Failure Catalog: A numbered list of 36 "silent failures"—bugs that compile perfectly but break the UI. Each entry includes the cause, symptom, and fix so the AI doesn't repeat past mistakes.The Subtraction Rule: I've banned the phrase "make it better" because AI usually interprets that as "add more decorative junk." I have a hard rule stating that "polish" means subtraction, backed by a list of rejected patterns.Doc-Code Sync: I have CI checks that fail if my documentation drifts from the source code. Lying docs are worse than no docs.Runtime Verification: I use a WebMCP agent to drive a real Chrome browser, clicking through pages and checking consoles across three different viewports to verify the AI's work.
Every cold session starts by feeding the AI these architecture docs and cheat sheets. This prevents the AI from skimming or giving those generic, lazy answers. I structure a ready-to-execute plan, and then a sequence of agents handles the implementation.
The trade-off? I've lost that "Eureka!" moment. That feeling of struggling for four hours and finally nailing a solution is gone, replaced by a confident "meh, the AI can handle it." I've effectively made it safe to be lazy, provided the system is rigorous enough to catch the errors.
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