Promptimus: Improving already good LLM prompts with zero manual engineering

Large language models (LLMs) have become integral to enterprise applications across industries. Under the hood, customers’ inputs to the models are usually augmented with prompts that encode intricate business logic, regulatory requirements, and domain expertise: a healthcare system must use language compliant with the Health Insurance Portability and Accountability Act, for instance, and a financial trading system must follow risk tolerance rules. These prompts are typically crafted by domain experts over weeks or months. Yet business demands continue to push for further performance gains. The challenge, therefore, is not engineering prompts from scratch but rather elevating already strong performance by discovering nuanced, task-specific refinements — without compromising domain requirements. It's model agnostic: It takes a prompt already optimized for a source model, rapidly reoptimizes it for a target model, and compares the optimized prompts across models. It's driven by performance criteria: It takes the existing prompt template, task-specific data samples, and user-defined performance metrics and generates targeted improvement strategies, iterating repeatedly to achieve domain-specific optimization objectives. It focuses on exploits: It uses a metric-analyzer AI agent to identify failure points and a debugging helper agent to identify root causes, and it surgically refines prompts relative to failures (rather than along…