TCQA ^2 : A Tiered Conversational Q & A Agent in Gaming (2025)

Abstract: This paper focuses on intelligent Q&A assistants in gaming, providing timely and accurate services by integrating structured game knowledge graphs, semi-structured FAQ pairs, and unstructured real-time online content. It offers personalized emotional companionship through customized virtual characters and provides gameplay guidance, data queries, and product recommendations through in-game tools. We propose a Tiered Conversational Q&A Agent (TCQA ^2 ), characterized by high precision, personalized chat, low response latency, efficient token cost and low-risk responses. Parallel modules in each tier cut latency via distributed tasks. Multiple retrievers and short-term memory boost multi-turn Q&A. Hallucination and safety checks improve response quality. Player tags and long-term memory enable personalization. Real-world evaluations show TCQA ^2 outperforms prompt-engineered LLMs and RAG-based agents in gaming Q&A, personalized dialogue, and risk mitigation.

## introduction with the rapid advancement of large language models (llms), intelligent applications like questionanswering chatbots and ai assistants have seen significant development @xcite . in gaming, non-player characters (npcs) are crucial for enriching narratives, guiding tasks, and enhancing player interaction @xcite . however, most current npcs are limited to specific dungeons or levels, offering fixed instructions without global game-knowledge question-answering, tool assistance, or prolonged memory-based dialogues. moreover, gaming terminology is highly specialized, and game lore and mechanics differ significantly from general domain knowledge. consequently, llms trained on generic datasets often underperform in game-related question-answering tasks. the retrieval-augmented generation @xcite ) method integrates domain-specific knowledge into llms. however, conventional rag frameworks face limitations in fusing multisource knowledge. agentic rag @xcite addresses this by dynamically managing retrieval strategies and refining contextual comprehension. while rag agents can answer questions based on context, they lack natural multi-turn conversation capabilities @xcite and struggle to adapt to personalized or personastylized interactions. to address the aforementioned challenges, we propose tcqa 2 for game-domain npc dialogues, which includes the following features: tcqa 2 employs a three-tiered architecture with parallel module execution, ensuring lowlatency interactions. its intent router dynamically allocates inquiries to designated agents, reducing token consumption from rag retrieval or redundant multi-module processing. ## methodology the integrated agent framework, as illustrated in figure 1 , adopts a three-tiered architecture: preprocess, generator and critic. pre-process layer handles preliminary data conditioning and intent routing. generator layer executes context-aware response generation. critic layer conducts multicriteria quality evaluation. ## related work traditional rag systems are limited by static workflows and poor adaptability to complex reasoning @xcite . agentic rag addresses these issues with autonomous agents that dynamically manage retrieval, refine context, and optimize workflows based on query complexity @xcite . its core paradigms include reflection, planning , tool use, and multi-agent collaboration. agentic rag excels in dynamic decision-making and sophisticated reasoning, delivering contextaware responses to new challenges. multi-agent frameworks in conversational qa systems decompose tasks for specialized agents, dynamically optimize workflows based on complexity (e.g., optima (rasooli and tetreault, 2015)), and integrate external tools (e.g., search engines, apis) to enhance retrieval and processing. personalized dialogue systems aim to improve user engagement by enabling agents to generate responses aligned with predefined character traits @xcite . rolellm integrates character role information through few-shot prompting, leveraging historical dialogue data to mimic character styles @xcite . integrating tool-use capabilities enables large language models (llms) to tackle complex problems more effectively. @xcite shows how multi-agent llms collaborate, leveraging diverse tools and expertise to mimic human workflows and enhance problem-solving efficiency. ## conclusion in this paper, we introduce a tiered conversational qa agent(tcqa 2 ) framework for intelligent assistants in the gaming domain. we adopt agentic rag technology to generate high-quality qa response, combine long/short memory with chitchat agent to achieve personalized conversation, and employ multi-granularity quality evaluation approach to ensure the safety of the responses. experimental results demonstrate that tcqa 2 significantly outperforms llm with prompt engineering and rag-based llms, particularly in scenarios requiring multi-source knowledge integration, contextual conversation personalization, and stringent response security protocols. current work still faces challenges such as insufficient support for multi-modal interaction and limitations in dynamic modeling of player profiles. future plans include expanding multi-modal input and output capabilities and enhancing the automated modeling of player behavior patterns.