LoRAX is an open-source inference server designed to serve large language models (LLMs) with support for multiple fine-tuned adapters on a single GPU, and its latest release, v0.8, offers native integration with the Outlines library for generating schema-compliant outputs. This is particularly useful for creating JSON outputs that adhere to specific schemas, which can be consumed by automated systems. The blog explores two core methods for generating JSON: structured generation and fine-tuning, demonstrating how each can enforce schema adherence or populate JSON with accurate content, respectively. By combining these methods, LoRAX achieves optimal results, producing outputs that are both structurally correct and content-accurate. A case study involving Named Entity Recognition (NER) tasks highlights the advantages of this combined approach, showing improved performance and reliability compared to using either method alone. The blog also addresses potential pitfalls, such as token limit issues and schema-model conflicts, underscoring the importance of aligning structured generation with model fine-tuning for optimal results.