๐ค๐ฉ๐ฝโ๐ณ Cook Like A Bot: AI dinner parties (part 1)
One year ago, I was 40 and didnโt know how to cook. So I set out on a journey to the most obvious solution: become a sous-chef to an AI. There have been highs and lows, yums and yucks, and lots of learning. Hereโs the story so far, and whatโs to come.
Intro
In June 2023, just one month into dating, my girlfriend Ava and I started hosting AI dinner parties in my small NYC apartment. AI would come up with a menu & recipes based on our inputs, which we cook & eat with friends, and rate the results. Intrigued by the personalization capabilities of Large Language Models (LLMs), we wanted a learning project that was fun, social, and delicious. Our initials are AI & ML, so it seemed like fate.
Recipe generation has been a popular LLM use case since the start of this AI boom. Initially I was running all kinds of ill-advised experiments, like juicing pulp โbreadโ for which I got roasted on Broadway. Fellow experimenters have also run other cool tests. Ava and I have kept going, pushing the boundaries, perhaps sometimes a bit overboard ๐คช.
[
, , , Sam Parr, WIRED]Below is the evolution of our AI dinner parties. We describe our System, Results from ๐น๐ญ๐ฎ๐น Thaitalian / ๐ฏ๐ต๐ฒ๐ฝ Japaxican / ๐ฌ๐ท๐ฐ๐ญ Greekbodian / ๐ฆ๐ค Friendsgiving dinners, Lessons learned, and whatโs Next.
System
The initial goals were to use AI to develop unique menus and recipes for social cooking nights, based on guest inputs, and intro AI to normies in a relatable way. ChatGPT helped brainstorm personalization variables like cuisine mashups, color focuses, feelings evoked, cooking techniques, holiday themes, ingredient textures, social causes, and even astrology signs. Here are the elements weโve explored so far:
Menu
We started with cuisine fusions because recipes generated were less likely to already exist in the AI training data. For the first few dinners, guests listed cuisines they wanted to cook, and we manually selected the two most requested. That evolved to ChatGPTโs Code Interpreter generating subjective-but-reasonable Compatibility and Novelty scores from 0โ100 for every possible fusion, then selecting MAX(Compatibility * Novelty ^ 2). Next we tried having set dishes (e.g. Thanksgiving traditional) modified based on each guestโs background and personality.
For dietary restrictions, we asked each guest about theirs and prompted AI to respect the limitations. Beyond vegan / keto / lactose-gluten-shellfish free, we collected limitations on ANIMALS (chicken / turkey / pork / octopus / squid / snails / clams / scallops / sea urchin), PLANTS (peanuts / mushrooms / avocado / seaweed / olives / bell peppers / lima beans / string beans), SEASONINGS (wasabi / ginger / caraway seeds / mint / cinnamon), self-aware semi-bans (โI try to stay away from cheese, but love itโ), and probably-not-but-good-to-knows (poi, live fish, โbig chunks of eggsโ).
After the no-nos, we asked guests for flavors they liked. Menus were generated around including wishlist ingredients (shrimp, aloe, anchovies, coconut milk, masa, bok choy, thick noodles, eggplant), spices (cumin, paprika, dill, coco aminos, jalapeรฑo, cardamom), and alcohols (aged rum, vodka, prosecco, margarita, classy gin, red wine). We also asked for mocktail vs cocktail prefs so the recipe would add booze only at the end.
AI also helped choose proteins. Guests voted amongst land (bison / pork / beef / rabbit), air (duck / chicken), and sea (fish / scallops / shellfish). Votes were ranked, restricted items were removed, and recipes were based around the remaining leaders. Plant proteins were also voted on, with at least one main course based on tofu / tempeh / seitan. These analyses required Code Interpreter to write a Python script: LLMs still suck at math.
Imagine expressing all these preferences to a restaurant, or hosting a party that respects every parameter. Thatโs hard for humans, but easy for AI. However, human judgment is still needed. Our first dinner party included a vegan friend, so AI made every course vegan. But sometimes you want to balance between needs of individuals and needs of groups. So we evolved the system to make sure that everyone had enough to eat, not every dish had to be for everyone, and it was clear what was what.
Recipes
Asking AI to generate recipes is easy, and average output is often fine. For a creative multi-course experience, more guidance is needed. We started by defining the courses: cocktail / appetizer / soup / salad / side / entree / dessert for most dinners, and turkey / mashed potatoes / gravy / stuffing / cranberry sauce / sweet potato / pumpkin pie for Friendsgiving. Over time we found that some courses benefitted from extra guidance like:
Cocktail: Make strong enough but not too strong, and include an interesting garnish on top.
Appetizer: Aim for finger food that can be eaten while standing and cooking. Cooking takes time and no one wants hangry guests.
Salad: Should have some texture (including something crunchy) and a tasty dressing.
AI also helped match proteins-courses-cuisines. A Chain of Thought approach worked best: ask for reasoning before recommendation. This helps AI โthinkโ before it โanswers.โ Hereโs an example of how AI worked out some logic, similar to how a human expert might think through it:
To make shopping easy, we avoided hard-to-find ingredients. After recipe generation, we asked AI to reflect on whether anything on the list would be rare in local large grocery stores. AI suggested which ingredients might need specialty stores, or offered alternatives. In rural areas this step could help with recipe practicality, and with enough notice most things can be ordered online.
We also had fun generating AI images of each dish in advance to tease our palate and compare with IRL final results (in Results section below). AI created image generation prompts in the style of professional food photography. Midjourney v5 was our initial tool, with great results, but was annoying to use within Discord. Later we switched to DALL-E 3 within ChatGPT: results were more variable, but it was easy to use inline. The power of integrated distribution! Weโve also tried generating images for each progressive recipe step (below). Consistency and realism are still highly variable, but this seems addressable soonish.
***
[Post split into parts due to Substack limits. Read the whole thing on Medium, or enjoy installments part 2 and part 3 in the coming days :)]
***
๐ป Website โข ๐ธ Instagram โข ๐ฆ X/Twitter โข ๐ผ LinkedIn โข ๐ Substack โข โซ Medium โข ๐บ YouTube โข ๐ฐ TikTok
***
Subscribe to
to receive (unforgettable human experiences) plus (Magical new capabilities) and (Niche AI transformations) ... or subscribe a la carte. Thanks for reading!