## Scenario You’re building an LLM-powered feature in a word processor (e.g., Microsoft Word) that generates content users can insert directly into a document (headings, bullets, tables, citations, styles, etc.). A common failure mode is that the LLM’s output **does not conform** to the required Word formatting/spec (wrong heading levels, broken lists, invalid table structure, missing citations, inconsistent styles). ## Task Design an approach to **calibrate and enforce** that the LLM’s generated content matches a target Word formatting specification. ### Requirements - Output must be valid according to a predefined schema (e.g., Word OpenXML subset or an internal document model). - Low latency for interactive generation. - Minimize “format drift” across revisions and multi-turn edits. - Provide a safe fallback when the model cannot comply. ### What to cover - What representation/schema you generate (e.g., structured JSON AST, XML, markdown-like intermediate form). - How you enforce constraints at generation time vs post-processing. - Training/fine-tuning or preference optimization options. - Validation, automatic repair, and human-in-the-loop strategies. - Metrics and offline/online evaluation.