Logic Engineering
Most of AI “engineering” today is cargo-cult theater.
People spend 90 % of their time:
stuffing 128 k tokens of context into the window praying something sticks
writing 400-line jailbreak prompts trying to trick the model into not hallucinating
It’s the equivalent of giving a junior dev Stack Overflow and saying “good luck.”
I just shipped something different.
I stopped treating the LLM like an oracle and started treating it like a 140-IQ intern who lies when bored.
I inserted a hard logic layer between the model and reality — a protocol that forces the model to do what actual senior engineers do first:
Shut up and gather every piece of evidence it might need
Explicitly list everything it still doesn’t know
Refuse to form a hypothesis until it, the model itself, signs off that context is exhaustive
Enumerate every plausible path, stress-test each for regressions, then pick one with receipts
No “think step by step” pleading. No RAG duct tape. Just engineered paranoia baked into the system prompt.
Result after 2,080 hours of daily use: 95 %+ reduction in hallucinations that actually ship to prod. Zero unrecoverable states. Zero “fix the fix the fix” death loops.
I’m calling the discipline Logic Engineering-- because Prompt Engineering and Context Engineering were never the whole problem.
The real problem was we kept giving the model freedom to skip the part human engineers never skip: obsessive, exhaustive evidence collection before thought.
The free generic version is at the bottom of the page. Paste it into Cursor, then watch the model suddenly grow a brain.
The full version for tuned explicitly for coding in IDEs adds the seat belts (backups, history, rollback).
But the free one already proves the thesis:
Give an LLM logic tools and it stops being a stochastic parrot and starts being an engineer.
We’ve been optimizing the wrong variable for three years.
Logic > tokens
https://gracefultc.gumroad.com/l/ctgyvz
—Elon would approve this message.
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