When using AI systems, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?

The recent improvements of AI after billions of iterations from the public launch of OpenAI has strengthened its results and capabilities. However, it has also increase the necessity of better prompts to achieve desired goals. In my personal experience, adapting my prompts to CREATE on more advanced requests has helped me narrow my scope to my specific needs and prepare a better risk assessment. On the other hand, I have shortened time consumption by teaching my team how to be more efficient using RTF instead of more advance prompts for easy requests.

Proper distinction of which framework aligns greater with your own requirements are clever for prompt engineering.

Additionally to the excellent points made, my suggestion would be to emphasize the critical importance of ensuring the user/requester possesses, or actively earns, at least fundamental knowledge in the specific area/domain before asking AI systems to assist with important or high-risk tasks.

While leveraging SMEs for final validation is key (as many have pointed out), having that foundational domain understanding yourself when interacting with the AI - crafting prompts, interpreting initial outputs, and guiding iterations - can be the difference between a theoretically "good" AI output and a practically applicable one. This is especially true when the AI is tasked with generating content that requires nuanced understanding or has significant downstream implications.