Community Prompting Workshop Recap: Craft an AI Template with Notably’s Founders

Working together to craft the magic behind Notably's AI-Powered Templates

Last Friday, we hosted an interactive event where we invited participants to roll up their sleeves and collaborate with us to create a new AI-powered Summary Template in Notably. We had so much fun during this workshop and appreciate our customers and community members for joining us!

Our co-founders Allison and Brittany kicked off the event by explaining why we decided to host this workshop. We have been releasing new AI Templates every week for our users and we love getting to pull back the curtain to reveal the behind the scenes work that goes into building these templates. 

The goal for the session? To have a working draft of an AI template created together with our community by its end. And guess what? We did it! 

This post outlines each section of the workshop, including examples from our community, helpful writing tips, and finally the output from the prompt we wrote together. You may watch the recording, follow along on the Figjam board, or continue reading for the highlights.

What are the building blocks of an AI Template?

We began the session by introducing Notably's AI Powered Templates – one that can be used directly on raw data to generate summaries (AI Summary Templates), and another that can be used on analyzed data to generate new insights and takeaways from your data (AI Insight Templates).

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Behind every single one of these templates is a carefully crafted prompt – essentially how you communicate with AI. You ask it a question; it gives you an answer. Each prompt is created using a System Prompt and a Main Prompt. 

System prompts are the initial set of instructions that serves as the starting point of a template. It “sets the mood”, and defines things to guide the overall behavior. A good system prompt answers questions such as: What role should the AI assume? What is the primary goal? What is the context? The quality and specificity of a system prompt has a significant impact on the output of the template. You want to ensure you are providing guardrails to focus the AI, but not constrain it. 

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Next comes the Main Prompt - this is where the creativity happens! Your system prompt has set the context, and your main prompt will define the specific tasks to be done. It is important to note that the same principles of effective human communication and collaboration apply to AI. So when writing your main prompt, be detailed, specific and direct. Make sure you frame your questions appropriately, tell the AI to format the content with Markdown, include references, structure and methods, and most importantly, get creative!

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Well known design thinking frameworks and research methods make a great backbone for effective prompts. In Notably, we have many templates for popular frameworks such as Jobs to be Done, Six Thinking Hats, and the 4 What’s.

In this workshop we collaborated with our community to create a new template based on the Rose Thorn Bud framework – categorizing feedback into 'rose' for positives, 'thorn' for negatives or challenges, and 'bud' for potential opportunities or ideas yet to bloom.

Crafting the Rose, Thorn, Bud Template

The Rose, Thorn, Bud framework is a simple framework used in design thinking to gather feedback and identify opportunities. It’s an approach to categorizing observations or ideas into three distinct buckets:

1. Rose - Represents the positive aspects or highlights of a particular subject or experience. It's what's going well or what participants appreciate.
2. Thorn- Refers to challenges, pain points, or areas of concern. These are things that aren't going well or could be considered problems or areas of friction.
3. Bud - Denotes potential opportunities or ideas that have not yet come to fruition but could be explored further. They're the seeds of future roses.

First, we tackled the system prompt together to set the context for the rest of the template. Participants in the workshop grabbed some sticky notes and began brainstorming the role, context, goal, Do’s and Don’ts, and tone of voice. 

Here are some examples from our Figjam board:

• Role: “A seasoned product designer”
• Context: “A sprint retrospective with the product team”
• Goal: “Figure out what is working, and what shouldn’t be changed, but rather extended”
• Do’s & Don’ts: “In the ‘thorn’ section, consider potential for abuse, misuse, and security concerns”
• Tone of Voice: “Be clear and concise, and avoid unnecessary words.”

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Then came crafting the main prompt where creativity was kicking in! Here specific tasks or questions are defined along with formatting instructions. Workshop participants added their thoughts around the format/structure, and questions / instructions to ask for each category: Rose, Thorn, Bud.

Here are some examples from our Figjam board:

• Format/Structure: “Create 3-5 bullets in each section”
• Rose: “From this data, which aspects of the product or experience represent things that are already working well?”
• Thorn: “Generate ‘How might we’ statements for each ‘thorn’”
• Bud: “List comparisons, analogies, or metaphors that users mentioned.”

We reviewed all of the stickies with the participants, and I worked live to draft the prompt using what we brainstormed! While I was pulling all the pieces together, Allison shared some helpful tips we have learned over the course of writing.

Here are some helpful tips:

1. Use the AI to help you write better prompts. Ask, “What additional input do you need from me to help you write a better output?”
2. Simplify complex responses. Say, “Explain this topic in simple terms a child could understand.”
3. Reduce “hallucination”, making claims that aren’t accurate or based in facts. Say, “Do not speculate or make assumptions. Use only the dataset provided.” 

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What was the output?

Being able to write a new prompt live with our users, add it to the app, and run the summary on real data during one session was incredible. Thanks to the high quality input from our participants in the workshop, our first draft prompt gave us a pretty nice output. You may watch the clip from the workshop below, or keep reading for the output.

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Here is the output our first draft generated from data about train travel:

Evaluative Research: Train Travel Experience

Rose 🌹

- The complimentary dessert offered by the train staff was a delightful gesture that exceeded expectations. 

• "The train staff presented us with a complimentary dessert. That was a delightful gesture on their part.”

- Despite some issues, there is an understanding and appreciation for the efforts of the train staff in managing crowded situations. 

• “I understand that trains can get crowded, and the staff do their best to manage it.”

- Planning ahead for potential delays helped mitigate stress associated with late arrival at the destination. 

• “We got to our destination late. But thankfully, you know, we planned for a buffer.”

‍Thorn 📌

- Long lines and waiting times were identified as areas of friction during the boarding process.

•  “My partner and I waited in a long line to board and find our seats.” 
• How might we expedite this process?

- Noise from large groups and children created a stressful environment, distracting from the romantic experience expected. 

• “In the observation car, large groups talking loudly and small children running around was pretty stressful.” 
• How might we create designated quiet zones or enforce noise control measures?

- Lack of timely announcements regarding delays caused additional stress. 

• “There were some delays, which were stressful due to lack of announcement.” 
• How might we improve communication about schedule changes?

Bud 🌱

- There’s an opportunity to enhance customer service by increasing staffing during peak travel times to help expedite the boarding processes.

- Implementing designated quiet zones or family-friendly cars could cater to different passenger needs and improve overall satisfaction.

- Improving real-time communication about delays or changes in schedule could alleviate passenger stress and uncertainty.

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This interactive event not only allowed us to demonstrate how our AI templates are developed but also gave participants hands-on experience in creating one themselves! By sharing this process openly, we hope to inspire more people to explore using AI in their research work while being completely transparent about our process working with AI in Notably.

After making some adjustments to our prompt, and multiple rounds of testing, this AI-Powered Template is now live in the app! We had so much fun collaborating with our users and I can’t wait to see which template we create together in our next workshop!

Be sure to watch the full recording here, sign up for free, and let us know which frameworks you'd like to see next in your Notably workspace.