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🐉 Dungeons & Dragons RAG Exploration 🎲

Exploring RAG techniques to create intelligent D&D assistants

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Welcome to the D&D RAG Research Project

This comprehensive project explores advanced Retrieval-Augmented Generation (RAG) techniques applied to Dungeons & Dragons content, including rulebooks, lore, and game mechanics. Our goal is to create an intelligent D&D assistant capable of understanding complex queries about game rules, character mechanics, and strategic gameplay.

🎯 Project Goals

  • Intelligent Query Understanding: Build systems that can parse complex D&D-related questions
  • Accurate Information Retrieval: Implement advanced techniques to find the most relevant game content
  • Context-Aware Responses: Generate answers that consider campaign context and player experience levels
  • Multi-Modal Knowledge: Integrate rules, lore, statistics, and strategic advice

🔬 Research Focus Areas

Our research spans multiple cutting-edge areas in AI and information retrieval:

Core RAG Optimization

Experimenting with chunk sizes, embedding models, and retrieval methods optimized for D&D content.

Advanced Retrieval Methods

Exploring query expansion, agentic tool use, and interactive retrieval for complex multi-step questions.

Knowledge Representation

Building graph-based systems and temporal knowledge graphs to capture D&D's interconnected mechanics.

AI Agent Capabilities

Implementing long-term memory and persistent context for ongoing campaign assistance.

Uncertainty and Reasoning

Developing confidence scoring, causal inference, and theory of mind modeling for more intelligent interactions.

🎲 Why D&D for RAG Research?

Dungeons & Dragons provides an ideal testbed for advanced RAG techniques because:

  • Complex Domain Knowledge: Rich interconnected rules, lore, and mechanics
  • Ambiguous Queries: Players often ask questions without precise terminology
  • Multi-Step Reasoning: Many questions require synthesizing information from multiple sources
  • Temporal Dynamics: Game narratives unfold over time, requiring understanding of story progression and cause-and-effect relationships
  • Contextual Personalization: Each campaign has unique house rules, character builds, and narrative contexts that demand adaptive responses