Hey there, fellow tech writers, educators, and content creators!
Lance here, and I've got some exciting insights to share about how I'm leveraging AI to revolutionize my design thinking coaching this summer.
Recently, I've been diving deep into the world of AI-assisted content creation, particularly in my work with District C, a design thinking education program.
The key to creating a truly helpful AI assistant isn't just about having a powerful tool – it's about understanding how to feed it the right knowledge.
What I've discovered is that the key to creating a truly helpful AI assistant isn't just about having a powerful tool – it's about understanding how to feed it the right knowledge.
(Even so, you can check out Boodlebox here.)
Check out the video above to see a demonstration!
In my experience, there are two crucial types of knowledge you need to consider when building your AI assistant:
Broad, Reusable Knowledge: This is the foundation of your AI's understanding. For me, it includes District C's philosophy, our teamship training slides, and general guidelines for students. This knowledge forms the backdrop against which all specific queries are interpreted.
Project-Specific Knowledge: This is the targeted information that makes your AI truly shine in specific contexts. In my case, it's things like problem scopes for individual clients or details about particular high school projects.
Understanding this distinction helps me target AI with the most appropriate context. By carefully curating these two layers of knowledge, I've been able to create an AI assistant that not only understands the broader context of design thinking but can also provide highly specific, relevant information for individual projects.
Here's a practical example:
When I need to write a short description of a business partner's problem for a pitch slide, I don't have to feed the AI all the background information. It already knows about District C's approach from the broad knowledge base. I just need to point it towards the specific project details, and voilà – it generates a tailored, contextually appropriate response.
But this approach isn't just about efficiency. It's about enhancing creativity and allowing for deeper, more meaningful work. By offloading the task of information retrieval and initial content generation to AI, I can focus on higher-level thinking and strategy.
As we continue to integrate AI into our professional lives, I believe this two-tiered approach to knowledge management will become increasingly crucial. It's not just about using AI; it's about using it intelligently.
I'm constantly refining this process, exploring how to structure information for optimal AI understanding. For instance, I've learned the importance of clear labeling within documents and creating dedicated files for key concepts that the AI might struggle to find otherwise.
This journey has completely transformed how I approach my design thinking projects. It's allowing me to be more responsive, creative, and effective in my coaching.
I'd love to hear your thoughts on this. How are you structuring knowledge for your AI assistants? What challenges have you encountered? Let's continue this conversation and push the boundaries of what's possible with AI-assisted work.
The future of content creation isn't just about having the latest AI tool – it's about knowing how to feed it the right knowledge.
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