Structured Output Prompting: Get Exactly the Format You Need

Start your prompt by declaring the output schema before the task. For example: 'Return a JSON object with keys: title (string), tags (array of strings), score (number 1–10). Then summarize this article: …' Putting the schema first anchors the model's generation toward your structure from token one.

Use constraint language to close ambiguity. Words like 'exactly', 'no other keys', 'use ISO 8601 for dates', and 'omit null fields' dramatically reduce hallucinated extras. If you need a numbered list, say 'return exactly 5 items, one per line, no preamble' — vague requests like 'make a list' leave the count and formatting up to the model.

On CinderHub you can paste a JSON schema or a filled-in example directly into the prompt as a template — the model treats a concrete example as a stronger constraint than a description alone. Pair this with a system instruction like 'You are a data extractor. Respond only with valid JSON, nothing else.' to eliminate prose wrappers and make downstream parsing reliable.