The Art of Asking Generative AI the Right Questions: What is Prompt Engineering?

In the field of Artificial Intelligence (AI), specifically Generative AI, prompt engineering is a strategy that focuses on designing strategic inputs or 'prompts' to guide machine learning models, such as a chatbot like Wonderflow's Wonder, toward the desired output. Instead of mere commands or questions, well-thought-out prompts, like a sort of soft programming, invoke specific AI responses, enhancing their accuracy and value. This strategy helps the model to emulate human-like reasoning, which is critical for user engagement and task completion. For instance, consider the task of making a Generative AI tool to generate a short story about a rabbit and a fox. Rather than a simple prompt like "Write a story about a rabbit and a fox," a more engineered prompt could be:

"Write a compelling short story for children aged 6-9 featuring a clever rabbit and a cunning fox, infused with themes of kindness and friendship."

This refined prompt not only specifies the characters but also gives context about the target age group, the tone (compelling), and the required themes (kindness and friendship). Such careful articulation aids the Generative AI model in generating a more precise, appropriate, and engaging response. Similarly, while asking for a summary, instead of a prompt like "Summarize this text", use a more detailed prompt such as:‍

"Write a concise, non-technical summary of the text suitable for a general audience"‍

The above can cause the AI to produce a response that is more suited to the needs of the user. Whether you're a total newbie at prompting or looking for quick guidance on how to build better business prompts, here's a checklist of tips to keep next to your chatbot. Bear in mind these tips are generalized and commonly reinstated to get the most practical outputs:

1. When requesting assistance from generative AI, it is important to provide specific and detailed information about the topic and provide context, such as the intended audience, tone, and timeframe.
2. Refining the output is an iterative process, and it is helpful to include an action or task for the AI to complete, such as summarizing, outlining, listing, planning, comparing, or predicting.
3. Parameters such as word count, exclusions, and formatting preferences should be set. It is important to avoid bias by phrasing prompts in a neutral manner that does not lead to a biased output.
   
   • Example (Bias prompt): Explain in three sentences why organic food is clearly overrated and offers no advantages over conventional food.
   • Example (Unbiased prompt): In a paragraph, summarize the pros and cons of consuming organic food instead of conventional food.

In conclusion, prompt engineering tailors Generative AI model responses for relevance, clarity, and depth, becoming a cornerstone of overall AI advancement. As AI continues to evolve, the careful crafting of prompts will garner greater importance, blending technology and linguistics to achieve enhanced user-oriented outputs. If you would like to learn more about this subject, it could interest you to check out leading examples of different Generative AI technologies disrupting today's industries.