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prompt-design

Prompt engineering is the practice of designing and optimizing prompts to effectively guide large language models (LLMs) and generative AI systems. It improves AI responses by using clear instructions, relevant context, examples, and constraints to achieve accurate and reliable outputs.

Here are 64 public repositories matching this topic...

A meta-prompting system that transforms raw prompts into production-ready, XML-structured prompts optimized for Claude Opus 4.6. 10 codified rules, 10-component framework, complexity-based routing — based on Anthropic's official best practices.

  • Updated May 28, 2026

SoftPrompt-IR is a low-level symbolic annotation layer for LLM prompts, making intent strength, direction, and priority explicit. It is not a DSL or framework, but a minimal, composable way to reduce ambiguity, improve safety, and structure prompts.

  • Updated Feb 11, 2026

PromptWeaver: RAG Edition helps design effective prompts for Traditional, Hybrid, and Agentic RAG systems. It offers templates, system prompts, and best practices to improve accuracy, context use, and LLM reasoning.

  • Updated Jul 25, 2026

Turn any raw prompt into a production-ready, XML-structured prompt optimized for Claude Opus 4.8 - 11 rules, complexity-based routing, hard prompt: trigger.

  • Updated May 28, 2026

Редакторский инструмент для естественного делового письма на русском без нейрояза, канцелярита, карьерных штампов и выдуманных деталей.

  • Updated Jul 10, 2026
  • Python

A framework for shaping identity-aware cognition in language models using behavioral prompt layering, recursive interpretive constraints, and modular cognitive modes.

  • Updated May 13, 2025

Object-Oriented Prompt Design (OOPD): オブジェクト指向型汎用プロンプト用語定義 (Object-Oriented Terminology for Prompt Design)

  • Updated Apr 10, 2025
  • Python