AI-generated content is output produced by an artificial-intelligence system from an input or instruction. It may appear as natural language, structured data, summaries, or programmatic results. section 12.1
Treat generated content as probabilistic rather than automatically factual. Models can hallucinate inaccurate or nonexistent information, while training data can introduce language bias and discrimination. section 1.6.3
Build a production-oriented workflow around generated content:
- Set clear objectives and frame the response context carefully. section 1.6.3
- Adjust relevant large language model parameters and cross-validate important outputs. section 1.6.3
- Add robust validation processes before generated content influences users or decisions. section 1.6.3
- Mitigate bias by restricting the knowledge base, selecting diverse training data, and auditing behavior with diverse inputs. section 1.6.3
- Use prompt engineering to tailor feedback and reviews to specific needs and improvement areas. section 10.5
Application outputs
A web application can combine prompts with tools such as file access, database queries, or API calls, then return a natural-language response, summary, structured result, or SQL output. section 12.1
