LLM Instruction Optimizer

Developers and power users building on LLMs struggle with models ignoring instructions, especially for specific output formats. This inconsistency breaks applications and forces users into frustrating, repetitive prompting to enforce rules, which often fails anyway, leading to wasted time and unreliable products.

A middleware service that automatically 'compiles' simple user rules into robust, battle-tested system prompts. It would use advanced techniques like strategic repetition, affirmative rephrasing, and weighted instructions to ensure the LLM consistently adheres to formatting and other constraints, providing developers with reliable, structured output without endless trial-and-error.