Prompt engineering and the responsible use of generative AI

The text discusses the importance of prompt design and engineering in using foundation models for productivity or supplementing them with proprietary data. Good prompts should minimize ambiguity, provide context, and follow principles such as giving directions, providing examples, formatting responses, dividing labor, and evaluating outputs iteratively. Poor prompt design can lead to misleading results, intellectual property theft, and malicious applications like hallucinations and prompt injections. To minimize hallucinations, fact-check results, ensure high-quality training sets, and use knowledge graphs for semantic relationships. Protecting generative AI involves data governance, security, anonymizing sensitive data, using access control policies, sanitizing prompts, screening outputs, and allowing end users to report bad results.