Technique: Structured Output Prompting
One of the most powerful capabilities of modern LLMs is generating structured data formats like JSON, XML, or CSV. This technique bridges the gap between natural language and machine-readable data — essential for building applications that consume AI output programmatically.
The key is to explicitly specify the output schema: name the keys, define expected data types, and provide the exact format you need. The more precise your schema definition, the more reliable the output. This is the foundation of “function calling” and “tool use” in modern AI agent architectures.
When to use: Data extraction from unstructured text, content classification, entity recognition, form auto-fill, API response generation, and any scenario where downstream code needs to parse the AI’s output.