📝 Type-safe LLM prompt templates for Rust — catch missing variables at compile time.
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.
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
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🚀
Design a system to classify customer sentiment reliably using LLM prompting techniques.
A comprehensive corpus of interconnected texts and protocols designed as a conceptual stress-test for advanced AI.
Self-hosted n8n automation pipelines for AI-driven market research.
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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