Automating legal document drafting can improve efficiency and reduce the burden of manual legal work. Yet, the structured generation of private legal documents remains underexplored, particularly in the Indian context, due to the scarcity of public datasets and the complexity of adapting models for long-form legal drafting. To address this gap, we introduce VidhikDastaavej, a large-scale, anonymized dataset of private legal documents curated in collaboration with an Indian law firm. Covering 133 diverse categories, this dataset is the first resource of its kind and provides a foundation for research in structured legal text generation and Legal AI more broadly. We further propose a Model-Agnostic Wrapper (MAW), a two-stage generation framework that first plans the section structure of a legal draft and then generates each section with retrieval-based prompts. MAW is independent of any specific LLM, making it adaptable across both open- and closed-source models. Comprehensive evaluation, including lexical, semantic, LLM-based, and expert-driven assessments with inter-annotator agreement, shows that the wrapper substantially improves factual accuracy, coherence, and completeness compared to fine-tuned baselines. This work establishes both a new benchmark dataset and a generalizable generation framework, paving the way for future research in AI-assisted legal drafting.

翻译：自动化法律文档起草能够提升效率并减少人工法律工作的负担。然而，由于公共数据集的匮乏以及为长篇幅法律起草需求适配模型的复杂性，结构化生成私人法律文档这一领域仍未被充分探索，尤其在印度场景下。为填补这一空白，我们推出了VidhikDastaavej——一个与印度某律师事务所合作策展的大规模、匿名化私人法律文档数据集。该数据集涵盖133个多样化类别，是同类资源中的首个，并为结构化法律文本生成及更广泛的司法人工智能研究奠定了基础。我们进一步提出一种模型无关的包装方法，这是一种两阶段生成框架：首先规划法律草案的章节结构，随后基于检索增强提示生成各章节内容。该包装方法不依赖于任何特定大语言模型，因此可适配开源与闭源模型。综合评估（包括词汇、语义、基于大语言模型及专家驱动的评估，并辅以注释者间一致性检验）表明，相比微调基线模型，该方法在事实准确性、连贯性和完整性方面有显著提升。本研究既建立了新的基准数据集，又提出了可泛化的生成框架，为人工智能辅助法律起草的未来研究铺平了道路。