Structured Output
Structured output is when a language model returns data in a predictable, machine-readable format—JSON, XML, typed objects—rather than free-form prose. This is what makes LLMs usable as components in software systems rather than just conversational interfaces. If you need the model to extract a name, date, and dollar amount from an invoice, you need those values in fields your code can parse, not embedded in a sentence. Most model providers now support constrained generation—forcing the model's output to conform to a JSON schema—which eliminates the parsing failures that plagued early integrations. OpenAI's structured output mode, Anthropic's tool use, and open-source libraries like Instructor all solve this problem. Structured output is the bridge between AI as a chat feature and AI as a system component, and getting it right is prerequisite to any serious automation.
Related terms:
Generative AI
Generative AI refers to AI systems that learn statistical patterns from training data to create new content—such as text, images, code, audio, or video—rather than classifying or analyzing existing data. This marks a shift from earlier discriminative models like spam filters and recommendation engines, with tools like ChatGPT, DALL-E, Midjourney, and Stable Diffusion driving its rapid mainstream adoption.
Zero-Shot Prompting
Zero-shot prompting is the most basic form of AI interaction where questions are posed without any examples or guidance, relying entirely on the model’s pre-trained knowledge. This baseline approach immediately tests raw capabilities, revealing both its breadth and limitations.
WWGPTD
WWGPTD began as internal Slack shorthand to remind teams that using AI isn’t cheating but the essential first step. The accompanying bracelets serve to normalize AI as a fundamental tool for creating better work.