Structuring LLM outputs: best practices for legal prompt engineering (2024) (studio.netdocuments.com)

This piece outlines practical prompt-engineering patterns for legal and contract workflows that make LLM outputs predictable and directly consumable by databases, spreadsheets, or downstream prompts. The guidance is significant for AI/ML practitioners because structured outputs reduce brittle parsing, cut manual cleanup, and enable reliable automation—critical in legal pipelines where date formats, monetary values, and entity fields must be exact. It’s particularly useful when chaining prompts or feeding outputs into contract lifecycle systems that demand strict formats. Key techniques include: giving concrete examples in the system prompt (e.g., “If the answer is a date, format as YYYY‑MM‑DD”); explicitly specifying labels and data types for each extracted field (Text, Number, Date, True/False, or custom types like Multiple Choice); and requesting common file formats such as CSV or JSON so results can be opened in Excel or ingested by code. Keep outputs terse by instructing “respond with only…” to eliminate filler. For advanced use, employ function-calling or structured-output schemas (OpenAI-style) to produce deterministic, machine-parseable results suitable for integration into downstream apps and automated pipelines.