The most headache inducing problem when integrating a large model into a real business system is often not "the model doesn't understand", but "the model output is uncontrollable" - the same question, returning JSON today and including a piece of prose tomorrow. Making the output stable and analyzable is a crucial step for prompt word engineering to move from beginner to advanced. Here are five practical methods that have been extensively tested in practice.
1、 Clearly specify the output format in the prompt words. Don't just say 'return result', but provide specific structural examples such as JSON field names, field types, and value ranges. The ability of models to imitate examples is much stronger than their adherence to abstract descriptions, with a 3-5 line example often surpassing a whole paragraph of formatting explanation.
2、 Use few shot examples to constrain style and boundaries. Providing two or more positive examples (preferably accompanied by another negative example) can significantly reduce the space for the model to freely express itself. Counterexamples are particularly effective: clearly tell the model not to output explanatory text, only output the data itself.
3、 Break down the big task into multiple rounds of small tasks. Instead of letting the model complete the entire process of "extraction classification summary" at once, it is better to break it down into independent sub calls, do only one thing in each round, and use the results of the previous round as input in the next round. The steps become shorter, the error surface becomes smaller, and the intermediate results can still be manually checked.
4、 Prioritize the use of the model's built-in controlled output capability. At present, the APIs of mainstream model vendors generally provide mechanisms such as JSON Mode, Structured Output, or Function Calling/Tool Use, with the server ensuring that the output conforms to predefined schemas. As long as the business allows, these official capabilities should be prioritized for use, rather than "praying" for the model to consciously follow the format in prompt words.
5、 Perform verification and retry at the application layer as a fallback measure. No matter how precise the prompt words are written, the output should still undergo schema validation online. If parsing fails, it should automatically retry (error messages can be attached for the model to correct on its own). Treating "model output" as "unreliable input" for defense is a fundamental skill in engineering.
One final reminder: the endpoint of structured output is stable and usable products, not the format itself. After writing the prompt words, it is recommended to use a batch of real inputs for regression testing, record the format pass rate, and continue iterating. Tools are improving, and prompt word techniques will also be updated, but the five principles of "clarity, examples, splitting, verification, and fallback" will not become outdated in the short term.
[Reference source] Comprehensive compilation of industry information and official API documents from mainstream big model vendors on common practices for JSON output and structured output.