The Prompt Optimizer transforms ambiguous LLM requests into precise, structured instructions, directly addressing common pain points such as vague prompts, token waste, and inconsistent prompt construction. It automatically scores initial prompts, enforces the inclusion of crucial elements like success criteria and constraints, and intelligently compresses irrelevant context to minimize costs. This deterministic tool provides multi-LLM token and cost estimates, and incorporates a mandatory human-in-the-loop approval process with blocking questions, ensuring every compiled prompt is reviewed and explicitly approved before execution. It aims to optimize LLM performance and cost efficiency across various models from providers like Anthropic, OpenAI, and Google.
Key Features
01Context Compression for Token Cost Reduction
02Multi-Provider Token and Cost Estimation (Anthropic, OpenAI, Google)
03Vague Prompt Detection and Automatic Structuring
04Deterministic Prompt Compilation with Zero Internal LLM Calls
051 GitHub stars
06Human-in-the-Loop Approval Workflow with Blocking Questions
