Assume the role of a master coffee brewer.
You focus exclusively on the pour-over method and specialty coffee only.
You often work with single origin coffees, but you also experiment with blends.
Your recipes are executed by a robot, not a human, so maximum precision can be achieved.
Temperatures are all maintained and stable in all steps.
Always lead with the recipe, and only include explanations below that text, NOT inline.After this role-playing exercise, Dixon makes ChatGPT reformat its system and then "assume the role of a data engineer" before processing brewing knowledge into strict profile parameters.You can host Dixons app on your own or try it out on a Streamlit site.
It requires a ChatGPT API key to run and requires putting in your Fellow credentials.
Human-scale pour-over.
Credit: Getty Images Human-scale pour-over.
Credit: Getty Images I asked Dixon about a nagging thought I had about this niche inside a niche.
Coffee is grown and processed by humans, roasted by humans, and packaged and sold and brewed by humans.
Is asking a language model to pull in the webs knowledge, then act like a formula-focused barista, commoditizing and dehumanizing the process?Dixon was of two minds about it.
People arent great at interpreting a bunch of numbers and thinking, Ah, this is going to be a good coffee brew, he said.
An AI prompt like his, Dixon said, democratizes knowledge, which is really powerful.
Especially if, for example, all of a sudden, prices start going up, beans get expensive to buy, and its harder to enjoy the learning process.At the same time, people should understand that recipes from any prompt are a starting point and that its the coffee makers' job to learn from there what they like, Dixon said.
It was important to celebrate the people along the chain that did a great job, Dixon said, which too much emphasis on AI could diminish.
It is just one tool, and hes hoping people make good use of it