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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 Dixon’s 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.
Turning coffee into numbers
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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 web’s knowledge, then act like a formula-focused barista, commoditizing and dehumanizing the process?
Dixon was of two minds about it. “People aren’t 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 it’s harder to enjoy the learning process.”
At the same time, people should understand that recipes from any prompt are “a starting point” and that it’s 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 he’s hoping people make good use of it
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