A framework to move beyond simple prompting towards defining *how* the LLM should structure its internal processing, access its latent knowledge, and apply specific heuristics or constraints when dealing with a particular subject matter or task.
📝 Type-safe LLM prompt templates for Rust — catch missing variables at compile time.
A universal, client-side AI prompt engineering tool that enhances your prompts using local or cloud-based AI models. Transform basic prompts into detailed, professional-grade instructions without sending your data to third-party servers.
运用提示词工程，驱动LLM生成HTML+CSS+JS互动教案，助力教师轻松备课，激发青少年编程学习兴趣与动手实践创造力🚀
A new package designed to facilitate the extraction of structured insights from user prompts related to the domain of autonomous AI agents and their potential vulnerabilities. Given an input text desc
Design a system to classify customer sentiment reliably using LLM prompting techniques.
Self-hosted n8n automation pipelines for AI-driven market research.
A comprehensive corpus of interconnected texts and protocols designed as a conceptual stress-test for advanced AI.
An LLM-powered pipeline for automated customer defect root cause analysis, structured classification, human review, and write-back in enterprise support systems.
Build type-safe LLM prompt templates for Rust with compile-time variable checks and clear runtime errors
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