In a recent episode by Boldpush on how Generative AI will impact the events industry, Chase went on to detail how Snack Prompt started, gave example prompts that event organizers could use to leverage generative AI in their events planning, and gave us some pro-tips on prompt engineering. In this post, I will discuss three key attributes of Snack Prompt, and share the prompt engineering advice I received.
About Snack Prompt
A Community-Driven Platform
I might be the only one who thinks its genius, but Snack Prompt thrives on a community-model where users actively participate by upvoting or downvoting prompts. This democratic approach ensures that the most intriguing and thought-provoking prompts rise to the surface, captivating the attention of AI enthusiasts and creators. This collective approach of community input creates an immediate measurement of the “usefulness” of posts shared. It not only shapes the use prominence of prompts but also fosters an environment where users collaborate to refine and elevate each other’s ideas. Getting feedback on prompts can help users improve them, snowballing into the best collaborative efforts in prompt engineering.
Organized by Topic
In classic forum style, you can easily navigate your prompt search by knowing what goal you want to achieve. Prompts designed for ChatGPT or MidJourney or Claude.ai can be filtered on, as well as further refined by the topic of your search or what you want to achieve with your prompt. Its wonderfully organized, and this makes it so much easier to navigate.
A New Marketplace for Creators
What I particularly appreciate about the Snack Prompt platform is how it has introduced a marketplace where creators can monetize their exceptional prompt designs. Those who craft particularly captivating and sought-after prompts have the opportunity to sell them to other users. This encourages creativity while also rewarding ingenuity, providing an incentive for users to create and share high-quality prompts that captivate the community. I think this is brilliant, and has strong potential as a staple platform for prompt engineers in future.
On Prompt Engineering
Dabbling with prompts myself, I paid extra attention to some of the feedback that Chase gave attendees on their questions of how to improve their prompting to get better results from whichever generative AI they are using. Here are my key takeaways:
- Be Clear and Concise: Being able to communicate well is a rare skill. If you want to write good prompts, you need to know how to structure your communication. Presenting a problem, your expectations of the solution, and providing an example or expected word count in the response is key to receiving good output.
- Give context: This one we surely know already, but giving context is not just about the information-environment that you’re working in. It includes small tricks such as starting a prompt with a verb instead of request words. For example “Create a seven day event schedule for a rock music concert” versus “Can you create a concert schedule for seven days?”. Providing an example of what you are looking for can further refine your prompt and clarify the length of text and the tone that you would like to communicate.
- Monitor your feedback: Monitor the feedback you give a bot to refine on the output you are receiving. While you may be tempted to respond saying “that was awful” or “you are not doing a good job” – this can risk the AI disregarding previous input information from you so as to readjust its processing and improve on the output. But this disregard of contextual information does not help refine the AI’s process. Instead, correct unwanted outputs by saying “I need you to adjust (x)” or “I am looking for more detail in (y) instead of (x)”.
If you’re an AI enthusiast, from novice to experienced, I invite you to checkout the Snack Prompt community and, who knows, maybe you’ll end up contributing to it too