Applying AI Concepts - Start With Your Context

We live in an age of abundant information thanks to artificial intelligence systems like ChatGPT. These systems can rapidly generate ideas, content, and recommendations on nearly any topic imaginable. However, as powerful as these AI tools are, the concepts they provide are often generalized and may not apply directly to your specific situation and context.

While AI can quickly provide answers, those answers lack the contextual alignment with our reality. We are woefully mistaken if we believe that simply putting a prompt into AI is going to magically align with our reality.

The danger comes from rushing to generalize AI-generated recommendations without first testing them in your environment. We have to create the conditions in which you can collect information to iterate and improve. The key is creating a safe space to convert the generalized information into ideas adapted for your context.

To address this disparity we put AI ideas “in the lab” first - design safe experiments in a controlled environment that represents your situation. See if the AI concept holds up or not. If it works - great, iterate and improve on it! If not, use the experience to understand what contextual gaps prevented success so you can try again.

Without taking the responsibility to translate general AI concepts into your specific context first, you risk misapplying recommendations in ways that could be ineffective or even harmful. So the next time you get promising ideas from ChatGPT or other AI systems. Bring those ideas into a safe space that simulates your environment, test them out, adapt them to your reality, and then determine how and where to apply them successfully. The contextual step is essential!