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@article{190200,
        author = {Shishir Govinda M and N Chaitra and Shiva Keerthi MP and Rohit Mesta and Savitha P},
        title = {RASE: Retrieval Augmented Story Engine Narration Control in game using AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {3195-3209},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190200},
        abstract = {Recent advances in large language models (LLMs) have enabled AI systems to generate increasingly coherent and contextually rich narratives. However, purely generative approaches to story generation often struggle with maintaining long-term coherence and factual consistency, sometimes producing disjointed or hallucinatory storylines . To address these challenges, researchers have begun integrating retrieval augmented generation (RAG) techniques into storytelling systems. In a Retrieval-Augmented Story Engine (RASE), a narrative-generating LLM is coupled with an external knowledge repository and retrieval mechanism, so that each piece of the story is grounded in relevant context fetched on demand. This framework leverages the strengths of both data-driven retrieval and generative modeling, aiming to reduce inconsistencies while preserving creativity . The following report examines RASE’s conceptual framework, core components, and implementation methodology, and situates it in the landscape of game AI and interactive narrative systems. We also compare RASE with alternative approaches – including purely generative narrative models, reinforcement learning-based storytelling agents, and classical game master or experience manager AIs – to clarify how retrieval augmentation builds upon and differs from these paradigms.},
        keywords = {RAG, SFT, Game, Narration, Machine Learning},
        month = {January},
        }